Master of Science in Artificial Intelligence

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, Naive Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the endto- end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the- art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decisionmaking processes in their professional careers.

This is a comprehensive course focused on the practical implementation of machine learning techniques across various industries. This course delves into the application of supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. Students will learn how to leverage these techniques to solve real-world problems in areas such as healthcare, finance, marketing, and beyond. Emphasis is placed on understanding the entire machine learning pipeline, from data preprocessing and model selection to evaluation and deployment. Throughout the course, students will engage in hands-on projects and case studies that demonstrate the practical use of machine learning in real-world scenarios. By applying machine learning algorithms to datasets, students will gain invaluable experience in extracting insights and making data-driven decisions. Additionally, the course covers best practices for model optimization and performance tuning, ensuring students are equipped to create robust and scalable machine learning solutions. By the end of the course, students will have a solid foundation in machine learning applications, empowering them to innovate and drive progress in their respective fields.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society. Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and criticalthinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

This is a course that focuses both on architectural design and practical hands-on learning of the most used cloud services. The module extensively uses Amazon Web services (AWS) to show real world code examples of various cloud services. It also covers the core concepts and architectures in a platform agnostic manner so that students can easily translate these learnings to other cloud platforms (like Azure, GCP etc.). The module starts with virtualization and how virtualized compute instances are created and configured. Students also learn how to auto-scale applications using load balancers and build fault tolerant applications across a geographically distributed cloud. As relational databases are widely used in most enterprises, students learn how to migrate and scale (both vertically and horizontally) these databases on the cloud while ensuring enterprise grade security. Virtual private clouds enable us to create a logically isolated virtual network of compute resources. Students learn to set up a VPC using virtualized-compute-servers on AWS. The course also covers the basics of networking while setting up a VPC. Students learn of the architecture and practical aspects of distributed object storage and how it enables low latency and high availability data storage on the cloud.

Upon completion of this course, you will gain a deep understanding of how business analytics supports data-driven decision-making in an evolving business landscape. You will explore key analytics frameworks, learning how organisations leverage data to navigate uncertainty and drive strategic growth. Through practical applications, you will differentiate between various data-driven techniques and examine their real-world implementation across industries such as banking and healthcare. Additionally, you will critically assess the challenges and ethical considerations of integrating analytics tools into business processes, equipping you to apply these insights effectively in your organisation.

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, Naive Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the endto- end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the- art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decisionmaking processes in their professional careers.

This course is designed to immerse students in the latest advancements and trends in AI. This course covers cutting-edge technologies such as deep learning, neural networks, natural language processing, computer vision, and reinforcement learning. Students will explore the innovative applications of these technologies in various domains, including healthcare, finance, robotics, and autonomous systems. The course emphasises not only understanding these technologies but also critically evaluating their potential and limitations.

Through a combination of theoretical insights and hands-on projects, students will gain practical experience with state-of-the-art AI tools and platforms. They will engage in experiments, case studies, and research activities that foster a deep appreciation of the current landscape and future directions of AI technology. By the end of the course, students will be well-equipped to contribute to the development and implementation of emerging AI solutions, positioning themselves at the forefront of technological innovation and advancement in the field of artificial intelligence.

This is a comprehensive course focused on the practical implementation of machine learning techniques across various industries. This course delves into the application of supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. Students will learn how to leverage these techniques to solve real-world problems in areas such as healthcare, finance, marketing, and beyond. Emphasis is placed on understanding the entire machine learning pipeline, from data preprocessing and model selection to evaluation and deployment. Throughout the course, students will engage in hands-on projects and case studies that demonstrate the practical use of machine learning in real-world scenarios. By applying machine learning algorithms to datasets, students will gain invaluable experience in extracting insights and making data-driven decisions. Additionally, the course covers best practices for model optimization and performance tuning, ensuring students are equipped to create robust and scalable machine learning solutions. By the end of the course, students will have a solid foundation in machine learning applications, empowering them to innovate and drive progress in their respective fields.

In this module we will discuss general approaches to the construction of efficient solutions to problems.

Such methods are of interest because:

1. They provide templates suited to solving a broad range of diverse problems.

2. They can be translated into common control and data structures provided by most high-level languages.

3. The temporal and spatial requirements of the algorithms which result can be precisely analyzed.

This course will provide a solid foundation and background to design and analysis of algorithms. In particular, upon successful completion of this course, students will be able to understand, explain and apply key algorithmic concepts and principles, which might include:

1. Greedy algorithms (Activity Selection, 0-1 Knapsack Problem, Fractional Knapsack Problem)

2. Dynamic programming (Longest Common Subsequence, 0-1 Knapsack Problem)

3. Minimum Spanning Trees (Prim’s Algorithm, Kruskal’s Algorithm)

4. Graph Algorithms (Dijkstra’s Shortest Path Algorithm, Bipartite Graphs, Minimum Vertex Cover)

Although more than one technique may be applicable to a specific problem, it is often the case that an algorithm constructed by one approach is clearly superior to equivalent solutions built using alternative techniques. This module will help students assess these choices.

Mathematics and computer science are closely related fields. Problems in computer science are often formalized and solved with mathematical methods. It is likely that many important problems currently facing computer scientists will be solved by researchers skilled in algebra, analysis, combinatorics, logic and/or probability theory, as well as computer science.

This course covers elementary discrete mathematics for computer science and engineering. Topics may include asymptotic notation and growth of functions; permutations and combinations; counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

Students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.

This course is dedicated to exploring the ethical, legal, and social implications of artificial intelligence technologies. This course examines key issues such as bias in AI algorithms, data privacy, transparency, accountability, and the impact of AI on employment and society. Students will engage with case studies and frameworks designed to address these challenges, learning how to develop and implement AI systems that align with ethical standards and promote fairness and inclusivity.

Through a combination of theoretical discussions and practical applications, the course equips students with the knowledge and tools necessary to navigate the complex landscape of AI ethics. Students will participate in discussions on policy, regulations, and best practices, and will work on projects that involve designing ethical AI solutions and conducting impact assessments. By the end of the course, students will be prepared to advocate for and implement ethical AI practices in their professional roles, ensuring that AI technologies are developed and used responsibly and equitably.

This course is designed to introduce students to the core concepts and methodologies of data science. This course covers a broad range of topics, including data collection, cleaning, and preprocessing, as well as statistical analysis, data visualisation, and exploratory data analysis. Students will learn how to apply various data science techniques to extract valuable insights from large datasets, empowering them to make data-driven decisions in diverse fields such as business, healthcare, and technology. Throughout the course, students will engage in practical exercises and projects that emphasise the application of data science principles to real-world problems. By working with actual datasets and using state-of-the-art tools and software, students will develop the skills necessary to analyse, interpret, and present data effectively. Upon completion of the course, students will have a strong foundation in data science, enabling them to leverage data to solve complex problems and drive innovation in their professional careers within the realm of artificial intelligence.

This is a foundational and mandatory course which aims to build student's ability to apply various algorithmic design methods to provide an optimal solution to computational problems. This course starts with time and space complexity analysis of divide and conquer algorithms using recursion-tree based methods and Master’s theorem. Students would also learn about amortized time and space complexity analysis for randomized/probabilistic algorithms. Various algorithmic design strategies would be introduced via real world examples and problems. Students would learn when, where and how to optimally use Divide and Conquer, Dynamic programming (top-down and button-up), Greedy, Backtracking and Randomization strategies with examples. The module uses various practical examples from Array manipulations, Sorting, Searching, String manipulations, Tree & Graphs traversals, Graph path-finding, Spanning Trees etc., to introduce the above algorithmic strategies in action. Students would implement many of the above algorithmic design methods from scratch as part of the assignments. The module also introduces how some of these popular algorithms are readily available via popular libraries in various programming languages.

This course is aimed to build a strong foundational knowledge of data structures (DS) used extensively in computing. The module starts with introducing time and space complexity notations and estimation for code snippets. This helps students be able to make trade-offs between various Data Structures while solving real world computational problems. The module introduces most widely used basic data structures like Dynamic arrays, multi-dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs. The module discusses multiple implementation variations for each of the above data-structures along with trade-offs in space and time for each implementation. In this course, students implement these data-structures from scratch to gain a solid understanding of their inner workings. Students are also introduced to how to use the built-in data-structures available in various programming languages/libraries like Python/NumPy/C++ STL/Java/JavaScript. Students solve real-world problems where they must use an optimal DS to solve a computational problem at hand.

This course provides a strong mathematical and applicative introduction to Deep Learning. The module starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularization by dropouts, batch normalization etc., to ensure that deep MLPs can be successfully trained. The module teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing.

This course is aimed at deepening students' understanding of cutting-edge topics in artificial intelligence. This course delves into advanced methodologies such as generative adversarial networks (GANs), meta-learning, and advanced reinforcement learning techniques. Students will explore the theoretical underpinnings and practical implementations of these sophisticated AI concepts, focusing on their applications in complex problem-solving and innovation across various domains.

Through a blend of advanced theoretical discussions and hands-on projects, students will engage with state-of-the-art tools and techniques, working on real-world problems and research projects. The course encourages critical thinking and problem-solving, preparing students to tackle the challenges of implementing and advancing AI technologies. By the end of the course, students will have a robust understanding of advanced AI concepts and be well-equipped to contribute to cutting-edge research and development in the field of artificial intelligence.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society. Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and criticalthinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

Data is the fuel driving all major organisations. This course helps you understand how to process data at scale. From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data. Students will learn a comprehensive view of the complete Data Engineering lifecycle.

This course provides a strategic and practical foundation for leading digital transformation in contemporary organisations. It equips learners with the tools to evaluate emerging technologies, manage digital disruption, and deliver successful transformation initiatives. Key frameworks help participants assess the risks and opportunities of adopting technologies such as AI, cloud computing, IoT, and blockchain, while distinguishing hype from real value. Drawing on Oxford research, the course explores critical success factors for implementation, including stakeholder engagement, communication, and trust-building. A significant focus is placed on cyber risk and resilience, with learners developing the skills to identify vulnerabilities, assess impact using the CIA triad, and lead with a cybersecurity mindset. The course also prepares learners to communicate risk effectively with senior leadership and align digital innovation with organisational strategy and values. Through scenario-based learning and applied exercises, participants build confidence to lead change, foster organisational resilience, and ensure that digital transformation efforts create long-term value and impact.

This course is focused on the advanced techniques and architectures used to build sophisticated AI systems. This course provides an in-depth exploration of neural networks, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning models. Students will gain a thorough understanding of how these models are designed, trained, and optimised to tackle complex tasks such as image recognition, natural language processing, and predictive analytics.

Through a combination of theoretical concepts and practical implementations, students will engage with cutting-edge tools and frameworks, such as TensorFlow and PyTorch, to develop and experiment with deep learning models. The course includes hands-on projects and case studies that highlight the application of neural networks in real-world scenarios, enabling students to build and fine-tune models for diverse applications. By the end of the course, students will be proficient in designing and deploying advanced neural network architectures, positioning themselves at the forefront of AI technology and innovation.

This course introduces the engineering discipline of client discovery and establishes the relational database foundations used throughout the program. It covers the structured communication and scoping artifacts used to translate an ambiguous business request into a defined technical problem, including problem framing, requirements documentation, and stakeholder-facing scoping documents. The course then addresses relational database competency from first principles: schema design, normalization, joins, aggregate queries, subqueries, and window functions, followed by query performance tuning through indexing and execution-plan analysis, and transactional integrity under concurrent access. Database security practices are covered, including row-level security, column-level encryption, and audit logging, for the protection of sensitive customer data. A central case study addresses secure access to data held in a legacy, network-isolated system, reflecting a common real-world enterprise constraint. The course closes with a survey of alternative data storage paradigms and foundational data-engineering concepts, along with practice presenting schema and data-access decisions to a non-technical stakeholder audience.

This course addresses professional software engineering practice in Python, moving beyond language syntax into the disciplines required for production-grade services. It covers a data-ingestion service that retrieves and processes enterprise data reliably at scale, using memory-efficient processing techniques, reusable design patterns, and object-oriented domain modeling. The course covers concurrent and asynchronous programming for parallel data processing, defensive error-handling and resilience patterns for unreliable data sources, and data validation and structured observability practices. Automated test suites and continuous integration are addressed alongside established software design principles and collaborative version-control workflows. The course concludes with an introduction to distributed data processing for handling data at scale, and includes technical communication specific to engineering teams, including code-review feedback and architecture decision documentation.

This course covers a complete, production-grade backend platform, addressing the full range of competencies required to operate an enterprise API. It covers a RESTful API incorporating authentication and authorization standards, including token-based authentication and role-based access control for multiple user types accessing the same system. The course addresses business-process logic modeled as a formal state machine, financial transactions and asynchronous notifications, and a documented, contract-tested API interface supporting dependent front-end systems. Real-time data delivery mechanisms are covered, along with caching and event-streaming architectures using Kafka for high-throughput, low-latency operations. The course addresses advanced competency in a relational database system, and introduces data-warehousing and transformation tools alongside workflow orchestration using Airflow and dbt to support downstream analytics. The course also includes a design review and incident documentation component for stakeholder communication.

This course covers working familiarity with modern front-end engineering sufficient for a functioning client interface and effective collaboration with front-end specialists. It addresses an interactive web application from foundational web and JavaScript concepts through modern component-based interface development, including state management, form handling, and integration with a live backend API. The course covers authentication and role-based access control as implemented on the client side, real-time data synchronization techniques, and testing and performance-evaluation methods for web applications, along with conceptual familiarity with modern rendering strategies. Rather than deep front-end specialization, the course is scoped to the awareness and vocabulary required to participate meaningfully in front-end architecture discussions and to defend interface decisions to both engineering and non-technical stakeholders, including through a structured end-user walkthrough component.

This course covers the design and deployment of applied generative-AI systems suitable for production use. It surveys the landscape of large-language-model providers and serving options, and addresses prompt-engineering practice treated as a versioned, tested engineering artifact rather than an informal exercise. The core of the course is a complete retrieval-augmented-generation system, covering document chunking, embedding, hybrid retrieval, and re-ranking. Systematic evaluation methods are applied to both the retrieval and generation components using quantitative metrics rather than informal assessment, alongside guardrails against sensitive data exposure and structured red-teaming exercises addressing adversarial manipulation. The course addresses cost and latency management for production AI systems, along with communication of AI system limitations, risk, and evaluation results to non-technical and compliance-oriented stakeholders.

This course extends generative-AI systems from passive question-answering into autonomous action within enterprise environments. It covers an AI agent capable of invoking external tools and taking real actions, progressing from a simple prototype to an orchestrated, stateful agent architecture with memory management and safeguards against common production failure modes. The course introduces a standardized protocol for exposing internal tools to AI systems, along with human-in-the-loop approval patterns for high-risk automated actions. A substantial portion of the course addresses enterprise systems integration: mapping an organization's existing systems landscape, adapters for legacy data formats and undocumented systems, and resilient connectors handling real-world integration failures such as rate limits and service outages. The course includes negotiation of agent autonomy boundaries with clients and coordination with client technical teams.

This course covers formal distributed-systems design in response to a large-scale traffic-growth scenario. It addresses a structured system-design methodology, including capacity estimation, applied to redesigning an existing platform for a substantial increase in load. The course covers architectural decomposition, including the trade-offs between monolithic and microservices designs, and distributed transaction patterns for maintaining consistency across services. It also covers load-balancing and caching strategies, the theoretical trade-offs between data consistency and availability, and practical strategies for scaling data storage, including database sharding, table partitioning, and distributed query processing for large datasets. Event-streaming architectures for high-throughput scenarios, scalable search infrastructure, and rate-limiting and distributed-identifier-generation techniques are covered, along with live, collaborative architectural whiteboarding.

This course applies the scalability challenges of the preceding course specifically to artificial-intelligence systems operating at enterprise volume. It addresses infrastructure for serving large-language-model inference at scale, including memory management, request batching, and capacity planning for specialized hardware, along with a routing layer distributing requests across multiple models based on cost and complexity. The course introduces a structured decision framework for determining when model fine-tuning is warranted over prompt-based approaches, and covers scaling embedding-generation pipelines and vector-search infrastructure to very large document and data volumes. A significant focus is AI system evaluation as ongoing production infrastructure rather than a one-time check, including continuous evaluation pipelines, experiment tracking, safe rollout strategies such as canary deployment for model and prompt changes, and fleet-wide AI observability for performance degradation. The course includes presenting AI infrastructure costs and strategic trade-offs to an executive audience.

This course covers the systems-administration and networking competencies required to prepare a production computing environment prior to application deployment. It addresses the Linux operating system, covering filesystem structure, user and permissions management, process management, and text-processing tools used for operational tasks such as log analysis. The course covers networking from foundational addressing and protocol concepts through network security hardening, including firewall configuration and encrypted communication, along with core transport-layer protocols and the standard networking reference model. Automation scripts for routine operational tasks, including health checks, backups, and scheduled maintenance, are covered, along with integration of scripting with higher-level programming tools for deployment automation. The course concludes with a technical-writing component addressing operational documentation intended to be usable by an unfamiliar on-call engineer.

This course covers containerization and container orchestration for a complete multi-service platform. It addresses production container images, container-security hardening practices, and container networking for multi-service communication. The course introduces container-orchestration-platform architecture in depth, covering workload deployment and update strategies, service networking and traffic routing, and automated scaling in response to load. Orchestration-platform security controls, including access-control policies and network segmentation, are covered, along with systematic troubleshooting for diagnosing failures in a running cluster. The course addresses the specific infrastructure requirements of AI inference workloads on an orchestration platform, including specialized hardware allocation, and concludes with declarative, version-controlled deployment automation. The course also includes communication of infrastructure-adoption decisions to a technical stakeholder and coordination of a live, multi-party production deployment.

This course covers the provisioning and operation of a complete production deployment on a major public cloud platform. It addresses cloud networking architecture, compute resource provisioning and security, and load balancing and storage services, including object storage and content-delivery infrastructure. The course covers competency in both relational and non-relational managed database services, in-memory caching infrastructure, and cloud-native observability and audit-logging tools. Managed container-orchestration service operation on the cloud platform, including networking and security configuration, is covered, along with infrastructure security practices across identity, network, and data-protection domains.The course concludes with infrastructure provisioning via an infrastructure-as-code tool and an automated continuous-integration and deployment pipeline, building on the manually-provisioned AWS foundation established earlier in the course. The course also covers cloud-cost communication with financial stakeholders and real-time incident coordination during a live production event.

This course covers engineering for reliability under real-world failure conditions. It addresses measurable reliability targets translated into operational policy, and system architecture for high availability across multiple geographic regions, including strategies for both fully active and standby failover configurations. The course covers disaster-recovery planning, including recovery objectives validated through structured failover drills, and the operational challenges of migrating and rebalancing sharded data systems under live production conditions. A central focus is chaos engineering: the deliberate, controlled introduction of failure into a system to verify graceful degradation rather than catastrophic failure. The course also extends prior container-orchestration knowledge with advanced extensibility and automation mechanisms, deepens cloud-networking competency for complex, multi-region architectures, and includes presenting reliability investment cases to business stakeholders.

This course covers the security-engineering and compliance competencies required for an enterprise system to earn organizational trust prior to deployment. It addresses structured threat-modeling methodology for identifying system vulnerabilities, and hands-on identification and remediation of common web-application security weaknesses. The course extends AI-specific security concerns beyond foundational guardrails into more advanced adversarial techniques, and covers secrets and certificate-management practices for production systems. Compliance posture mapping against recognized regulatory and industry frameworks is covered, along with structured penetration-testing methodology using industry-standard tooling. The course addresses incident-response planning using a recognized framework, including a live simulation component involving detection and coordinated response to an in-progress security incident, followed by communication of security posture and incident outcomes to an executive audience.

This capstone course covers independent application of the full range of program competencies to an entirely new client engagement, under realistic time constraints and without the benefit of an established, continuously evolving system. It addresses client discovery, project scoping, and a complete, compressed full-stack system — spanning data architecture, backend services, a client interface, and an applied AI feature. The course includes structured defense of technical decisions under peer review, a professional technical portfolio and case study, and system-design and behavioral interview scenarios, concluding with a final project demonstration to an external panel of industry evaluators.

This is a course that focuses both on architectural design and practical hands-on learning of the most used cloud services. The module extensively uses Amazon Web services (AWS) to show real world code examples of various cloud services. It also covers the core concepts and architectures in a platform agnostic manner so that students can easily translate these learnings to other cloud platforms (like Azure, GCP etc.). The module starts with virtualization and how virtualized compute instances are created and configured. Students also learn how to auto-scale applications using load balancers and build fault tolerant applications across a geographically distributed cloud. As relational databases are widely used in most enterprises, students learn how to migrate and scale (both vertically and horizontally) these databases on the cloud while ensuring enterprise grade security. Virtual private clouds enable us to create a logically isolated virtual network of compute resources. Students learn to set up a VPC using virtualized-compute-servers on AWS. The course also covers the basics of networking while setting up a VPC. Students learn of the architecture and practical aspects of distributed object storage and how it enables low latency and high availability data storage on the cloud.

This course focuses on building basic classification and regression models and understanding these models rigorously both with a mathematical and an applicative focus. The module starts with a basic introduction to high dimensional geometry of points, distance-metrics, hyperplanes and hyperspheres. We build on top this to introduce the mathematical formulation of logistic regression to find a separating hyperplane. Students learn to solve the optimization problem using vector calculus and gradient descent (GD) based algorithms. The module introduces computational variations of GD like mini-batch and stochastic gradient descent. Students also learn other popular classification and regression methods like k-Nearest Neighbours, Naive Bayes, Decision Trees, Linear Regression etc. Students also learn how each of these techniques under various real world situations like the presence of outliers, imbalanced data, multi class classification etc. Students learn bias and variance trade-off and various techniques to avoid overfitting and underfitting. Students also study these algorithms from a Bayesian viewpoint along with geometric intuition. This module is hands-on and students apply all these classical techniques to real world problems.

This course is designed to bridge the gap between data theory and real-world applications. This course focuses on the endto- end process of data analytics, including data collection, cleaning, exploratory data analysis, and visualisation. Students will learn how to apply statistical methods and machine learning techniques to analyse and interpret complex datasets, uncovering actionable insights that drive strategic decision-making across various domains such as business, healthcare, and technology.

The course combines theoretical instruction with hands-on projects, allowing students to work with real datasets and employ state-of-the- art tools and software. By engaging in case studies and practical exercises, students will develop the skills necessary to tackle data-driven problems and present their findings effectively. Upon completion, students will be well-equipped to leverage data analytics to solve real-world challenges and contribute to data-informed decisionmaking processes in their professional careers.

This course is designed to immerse students in the latest advancements and trends in AI. This course covers cutting-edge technologies such as deep learning, neural networks, natural language processing, computer vision, and reinforcement learning. Students will explore the innovative applications of these technologies in various domains, including healthcare, finance, robotics, and autonomous systems. The course emphasises not only understanding these technologies but also critically evaluating their potential and limitations.

Through a combination of theoretical insights and hands-on projects, students will gain practical experience with state-of-the-art AI tools and platforms. They will engage in experiments, case studies, and research activities that foster a deep appreciation of the current landscape and future directions of AI technology. By the end of the course, students will be well-equipped to contribute to the development and implementation of emerging AI solutions, positioning themselves at the forefront of technological innovation and advancement in the field of artificial intelligence.

This is a comprehensive course focused on the practical implementation of machine learning techniques across various industries. This course delves into the application of supervised and unsupervised learning algorithms, including regression, classification, clustering, and dimensionality reduction. Students will learn how to leverage these techniques to solve real-world problems in areas such as healthcare, finance, marketing, and beyond. Emphasis is placed on understanding the entire machine learning pipeline, from data preprocessing and model selection to evaluation and deployment. Throughout the course, students will engage in hands-on projects and case studies that demonstrate the practical use of machine learning in real-world scenarios. By applying machine learning algorithms to datasets, students will gain invaluable experience in extracting insights and making data-driven decisions. Additionally, the course covers best practices for model optimization and performance tuning, ensuring students are equipped to create robust and scalable machine learning solutions. By the end of the course, students will have a solid foundation in machine learning applications, empowering them to innovate and drive progress in their respective fields.

In this module we will discuss general approaches to the construction of efficient solutions to problems.

Such methods are of interest because:

1. They provide templates suited to solving a broad range of diverse problems.

2. They can be translated into common control and data structures provided by most high-level languages.

3. The temporal and spatial requirements of the algorithms which result can be precisely analyzed.

This course will provide a solid foundation and background to design and analysis of algorithms. In particular, upon successful completion of this course, students will be able to understand, explain and apply key algorithmic concepts and principles, which might include:

1. Greedy algorithms (Activity Selection, 0-1 Knapsack Problem, Fractional Knapsack Problem)

2. Dynamic programming (Longest Common Subsequence, 0-1 Knapsack Problem)

3. Minimum Spanning Trees (Prim’s Algorithm, Kruskal’s Algorithm)

4. Graph Algorithms (Dijkstra’s Shortest Path Algorithm, Bipartite Graphs, Minimum Vertex Cover)

Although more than one technique may be applicable to a specific problem, it is often the case that an algorithm constructed by one approach is clearly superior to equivalent solutions built using alternative techniques. This module will help students assess these choices.

Mathematics and computer science are closely related fields. Problems in computer science are often formalized and solved with mathematical methods. It is likely that many important problems currently facing computer scientists will be solved by researchers skilled in algebra, analysis, combinatorics, logic and/or probability theory, as well as computer science.

This course covers elementary discrete mathematics for computer science and engineering. Topics may include asymptotic notation and growth of functions; permutations and combinations; counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.

Students will be able to explain and apply the basic methods of discrete (noncontinuous) mathematics in computer science. They will be able to use these methods in subsequent courses in the design and analysis of algorithms, computability theory, software engineering, and computer systems.

This course is dedicated to exploring the ethical, legal, and social implications of artificial intelligence technologies. This course examines key issues such as bias in AI algorithms, data privacy, transparency, accountability, and the impact of AI on employment and society. Students will engage with case studies and frameworks designed to address these challenges, learning how to develop and implement AI systems that align with ethical standards and promote fairness and inclusivity.

Through a combination of theoretical discussions and practical applications, the course equips students with the knowledge and tools necessary to navigate the complex landscape of AI ethics. Students will participate in discussions on policy, regulations, and best practices, and will work on projects that involve designing ethical AI solutions and conducting impact assessments. By the end of the course, students will be prepared to advocate for and implement ethical AI practices in their professional roles, ensuring that AI technologies are developed and used responsibly and equitably.

This course is designed to introduce students to the core concepts and methodologies of data science. This course covers a broad range of topics, including data collection, cleaning, and preprocessing, as well as statistical analysis, data visualisation, and exploratory data analysis. Students will learn how to apply various data science techniques to extract valuable insights from large datasets, empowering them to make data-driven decisions in diverse fields such as business, healthcare, and technology. Throughout the course, students will engage in practical exercises and projects that emphasise the application of data science principles to real-world problems. By working with actual datasets and using state-of-the-art tools and software, students will develop the skills necessary to analyse, interpret, and present data effectively. Upon completion of the course, students will have a strong foundation in data science, enabling them to leverage data to solve complex problems and drive innovation in their professional careers within the realm of artificial intelligence.

This is a foundational and mandatory course which aims to build student's ability to apply various algorithmic design methods to provide an optimal solution to computational problems. This course starts with time and space complexity analysis of divide and conquer algorithms using recursion-tree based methods and Master’s theorem. Students would also learn about amortized time and space complexity analysis for randomized/probabilistic algorithms. Various algorithmic design strategies would be introduced via real world examples and problems. Students would learn when, where and how to optimally use Divide and Conquer, Dynamic programming (top-down and button-up), Greedy, Backtracking and Randomization strategies with examples. The module uses various practical examples from Array manipulations, Sorting, Searching, String manipulations, Tree & Graphs traversals, Graph path-finding, Spanning Trees etc., to introduce the above algorithmic strategies in action. Students would implement many of the above algorithmic design methods from scratch as part of the assignments. The module also introduces how some of these popular algorithms are readily available via popular libraries in various programming languages.

This course is aimed to build a strong foundational knowledge of data structures (DS) used extensively in computing. The module starts with introducing time and space complexity notations and estimation for code snippets. This helps students be able to make trade-offs between various Data Structures while solving real world computational problems. The module introduces most widely used basic data structures like Dynamic arrays, multi-dimensional arrays, Lists, Strings, Hash Tables, Binary Trees, Balanced Binary Trees, Priority Queues and Graphs. The module discusses multiple implementation variations for each of the above data-structures along with trade-offs in space and time for each implementation. In this course, students implement these data-structures from scratch to gain a solid understanding of their inner workings. Students are also introduced to how to use the built-in data-structures available in various programming languages/libraries like Python/NumPy/C++ STL/Java/JavaScript. Students solve real-world problems where they must use an optimal DS to solve a computational problem at hand.

This course provides a strong mathematical and applicative introduction to Deep Learning. The module starts with the perceptron model as an over simplified approximation to a biological neuron. We motivate the need for a network of neurons and how they can be connected to form a Multi Layered Perceptron (MLPs). This is followed by a rigorous understanding of back-propagation algorithms and its limitations from the 1980s. Students study how modern deep learning took off with improved computational tools and data sets. We teach more modern activation units (like ReLU and SeLU) and how they overcome problems with the more classical Sigmoid and Tanh units. Students learn weight initialization methods, regularization by dropouts, batch normalization etc., to ensure that deep MLPs can be successfully trained. The module teaches variants of Gradient Descent that have been specifically designed to work well for deep learning systems like ADAM, AdaGrad, RMSProp etc. Students also learn AutoEncoders, VAEs and Word2Vec as unsupervised, encoding deep-learning architectures. We apply all of the foundational theory learned to various real world problems using TensorFlow 2 and Keras. Students also understand how TensorFlow 2 works internally with specific focus on computational graph processing.

This course is aimed at deepening students' understanding of cutting-edge topics in artificial intelligence. This course delves into advanced methodologies such as generative adversarial networks (GANs), meta-learning, and advanced reinforcement learning techniques. Students will explore the theoretical underpinnings and practical implementations of these sophisticated AI concepts, focusing on their applications in complex problem-solving and innovation across various domains.

Through a blend of advanced theoretical discussions and hands-on projects, students will engage with state-of-the-art tools and techniques, working on real-world problems and research projects. The course encourages critical thinking and problem-solving, preparing students to tackle the challenges of implementing and advancing AI technologies. By the end of the course, students will have a robust understanding of advanced AI concepts and be well-equipped to contribute to cutting-edge research and development in the field of artificial intelligence.

This is a project-based course, with the aim of building the required skills for creating web-based software systems. The course covers the entire lifecycle of building software projects, from requirement gathering and scope definition from a product document, to designing the architecture of the system, and all the way to delivery and maintenance of the software system.

The course covers both frontend, which is, building browser-based interfaces for users, using frontend web frameworks, and also building the backend, which is the server running an API to serve the information to the frontend, and running on an SQL or similar database management system for storage.

All aspects of delivering a software project, including security, user authentication and authorisation, monitoring and analytics, and maintaining the project are covered. The course also covers the aspects of project maintenance, like using a version control system, setting up continuous integration and deployment pipelines and bug trackers.

This course is designed to provide students with a comprehensive overview of the key concepts, techniques, and applications of AI. This course covers the history and evolution of AI, fundamental theories, and essential algorithms, including search methods, knowledge representation, machine learning, and neural networks. Students will explore the practical applications of AI in various domains such as robotics, natural language processing, computer vision, and expert systems, gaining an understanding of how AI technologies are transforming industries and society. Through a mix of theoretical lectures and hands-on exercises, students will develop a solid grounding in AI principles and practices. They will engage in projects and case studies that illustrate real-world AI applications, enhancing their problem-solving and criticalthinking skills. By the end of the course, students will have a thorough understanding of AI fundamentals and be prepared to delve deeper into specialised AI topics, positioning themselves for success in advanced courses and professional roles within the field of artificial intelligence.

Data is the fuel driving all major organisations. This course helps you understand how to process data at scale. From understanding the fundamentals of distributed processing to designing data warehousing and writing ETL (Extract Transform Load) pipelines to process batch and streaming data. Students will learn a comprehensive view of the complete Data Engineering lifecycle.

This course is focused on the advanced techniques and architectures used to build sophisticated AI systems. This course provides an in-depth exploration of neural networks, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning models. Students will gain a thorough understanding of how these models are designed, trained, and optimised to tackle complex tasks such as image recognition, natural language processing, and predictive analytics.

Through a combination of theoretical concepts and practical implementations, students will engage with cutting-edge tools and frameworks, such as TensorFlow and PyTorch, to develop and experiment with deep learning models. The course includes hands-on projects and case studies that highlight the application of neural networks in real-world scenarios, enabling students to build and fine-tune models for diverse applications. By the end of the course, students will be proficient in designing and deploying advanced neural network architectures, positioning themselves at the forefront of AI technology and innovation.

This course introduces the engineering discipline of client discovery and establishes the relational database foundations used throughout the program. It covers the structured communication and scoping artifacts used to translate an ambiguous business request into a defined technical problem, including problem framing, requirements documentation, and stakeholder-facing scoping documents. The course then addresses relational database competency from first principles: schema design, normalization, joins, aggregate queries, subqueries, and window functions, followed by query performance tuning through indexing and execution-plan analysis, and transactional integrity under concurrent access. Database security practices are covered, including row-level security, column-level encryption, and audit logging, for the protection of sensitive customer data. A central case study addresses secure access to data held in a legacy, network-isolated system, reflecting a common real-world enterprise constraint. The course closes with a survey of alternative data storage paradigms and foundational data-engineering concepts, along with practice presenting schema and data-access decisions to a non-technical stakeholder audience.

This course addresses professional software engineering practice in Python, moving beyond language syntax into the disciplines required for production-grade services. It covers a data-ingestion service that retrieves and processes enterprise data reliably at scale, using memory-efficient processing techniques, reusable design patterns, and object-oriented domain modeling. The course covers concurrent and asynchronous programming for parallel data processing, defensive error-handling and resilience patterns for unreliable data sources, and data validation and structured observability practices. Automated test suites and continuous integration are addressed alongside established software design principles and collaborative version-control workflows. The course concludes with an introduction to distributed data processing for handling data at scale, and includes technical communication specific to engineering teams, including code-review feedback and architecture decision documentation.

This course covers a complete, production-grade backend platform, addressing the full range of competencies required to operate an enterprise API. It covers a RESTful API incorporating authentication and authorization standards, including token-based authentication and role-based access control for multiple user types accessing the same system. The course addresses business-process logic modeled as a formal state machine, financial transactions and asynchronous notifications, and a documented, contract-tested API interface supporting dependent front-end systems. Real-time data delivery mechanisms are covered, along with caching and event-streaming architectures using Kafka for high-throughput, low-latency operations. The course addresses advanced competency in a relational database system, and introduces data-warehousing and transformation tools alongside workflow orchestration using Airflow and dbt to support downstream analytics. The course also includes a design review and incident documentation component for stakeholder communication.

This course covers working familiarity with modern front-end engineering sufficient for a functioning client interface and effective collaboration with front-end specialists. It addresses an interactive web application from foundational web and JavaScript concepts through modern component-based interface development, including state management, form handling, and integration with a live backend API. The course covers authentication and role-based access control as implemented on the client side, real-time data synchronization techniques, and testing and performance-evaluation methods for web applications, along with conceptual familiarity with modern rendering strategies. Rather than deep front-end specialization, the course is scoped to the awareness and vocabulary required to participate meaningfully in front-end architecture discussions and to defend interface decisions to both engineering and non-technical stakeholders, including through a structured end-user walkthrough component.

This course covers the design and deployment of applied generative-AI systems suitable for production use. It surveys the landscape of large-language-model providers and serving options, and addresses prompt-engineering practice treated as a versioned, tested engineering artifact rather than an informal exercise. The core of the course is a complete retrieval-augmented-generation system, covering document chunking, embedding, hybrid retrieval, and re-ranking. Systematic evaluation methods are applied to both the retrieval and generation components using quantitative metrics rather than informal assessment, alongside guardrails against sensitive data exposure and structured red-teaming exercises addressing adversarial manipulation. The course addresses cost and latency management for production AI systems, along with communication of AI system limitations, risk, and evaluation results to non-technical and compliance-oriented stakeholders.

This course extends generative-AI systems from passive question-answering into autonomous action within enterprise environments. It covers an AI agent capable of invoking external tools and taking real actions, progressing from a simple prototype to an orchestrated, stateful agent architecture with memory management and safeguards against common production failure modes. The course introduces a standardized protocol for exposing internal tools to AI systems, along with human-in-the-loop approval patterns for high-risk automated actions. A substantial portion of the course addresses enterprise systems integration: mapping an organization's existing systems landscape, adapters for legacy data formats and undocumented systems, and resilient connectors handling real-world integration failures such as rate limits and service outages. The course includes negotiation of agent autonomy boundaries with clients and coordination with client technical teams.

This course covers formal distributed-systems design in response to a large-scale traffic-growth scenario. It addresses a structured system-design methodology, including capacity estimation, applied to redesigning an existing platform for a substantial increase in load. The course covers architectural decomposition, including the trade-offs between monolithic and microservices designs, and distributed transaction patterns for maintaining consistency across services. It also covers load-balancing and caching strategies, the theoretical trade-offs between data consistency and availability, and practical strategies for scaling data storage, including database sharding, table partitioning, and distributed query processing for large datasets. Event-streaming architectures for high-throughput scenarios, scalable search infrastructure, and rate-limiting and distributed-identifier-generation techniques are covered, along with live, collaborative architectural whiteboarding.

This course applies the scalability challenges of the preceding course specifically to artificial-intelligence systems operating at enterprise volume. It addresses infrastructure for serving large-language-model inference at scale, including memory management, request batching, and capacity planning for specialized hardware, along with a routing layer distributing requests across multiple models based on cost and complexity. The course introduces a structured decision framework for determining when model fine-tuning is warranted over prompt-based approaches, and covers scaling embedding-generation pipelines and vector-search infrastructure to very large document and data volumes. A significant focus is AI system evaluation as ongoing production infrastructure rather than a one-time check, including continuous evaluation pipelines, experiment tracking, safe rollout strategies such as canary deployment for model and prompt changes, and fleet-wide AI observability for performance degradation. The course includes presenting AI infrastructure costs and strategic trade-offs to an executive audience.

This course covers the systems-administration and networking competencies required to prepare a production computing environment prior to application deployment. It addresses the Linux operating system, covering filesystem structure, user and permissions management, process management, and text-processing tools used for operational tasks such as log analysis. The course covers networking from foundational addressing and protocol concepts through network security hardening, including firewall configuration and encrypted communication, along with core transport-layer protocols and the standard networking reference model. Automation scripts for routine operational tasks, including health checks, backups, and scheduled maintenance, are covered, along with integration of scripting with higher-level programming tools for deployment automation. The course concludes with a technical-writing component addressing operational documentation intended to be usable by an unfamiliar on-call engineer.

This course covers containerization and container orchestration for a complete multi-service platform. It addresses production container images, container-security hardening practices, and container networking for multi-service communication. The course introduces container-orchestration-platform architecture in depth, covering workload deployment and update strategies, service networking and traffic routing, and automated scaling in response to load. Orchestration-platform security controls, including access-control policies and network segmentation, are covered, along with systematic troubleshooting for diagnosing failures in a running cluster. The course addresses the specific infrastructure requirements of AI inference workloads on an orchestration platform, including specialized hardware allocation, and concludes with declarative, version-controlled deployment automation. The course also includes communication of infrastructure-adoption decisions to a technical stakeholder and coordination of a live, multi-party production deployment.

This course covers the provisioning and operation of a complete production deployment on a major public cloud platform. It addresses cloud networking architecture, compute resource provisioning and security, and load balancing and storage services, including object storage and content-delivery infrastructure. The course covers competency in both relational and non-relational managed database services, in-memory caching infrastructure, and cloud-native observability and audit-logging tools. Managed container-orchestration service operation on the cloud platform, including networking and security configuration, is covered, along with infrastructure security practices across identity, network, and data-protection domains.The course concludes with infrastructure provisioning via an infrastructure-as-code tool and an automated continuous-integration and deployment pipeline, building on the manually-provisioned AWS foundation established earlier in the course. The course also covers cloud-cost communication with financial stakeholders and real-time incident coordination during a live production event.

This course covers engineering for reliability under real-world failure conditions. It addresses measurable reliability targets translated into operational policy, and system architecture for high availability across multiple geographic regions, including strategies for both fully active and standby failover configurations. The course covers disaster-recovery planning, including recovery objectives validated through structured failover drills, and the operational challenges of migrating and rebalancing sharded data systems under live production conditions. A central focus is chaos engineering: the deliberate, controlled introduction of failure into a system to verify graceful degradation rather than catastrophic failure. The course also extends prior container-orchestration knowledge with advanced extensibility and automation mechanisms, deepens cloud-networking competency for complex, multi-region architectures, and includes presenting reliability investment cases to business stakeholders.

This course covers the security-engineering and compliance competencies required for an enterprise system to earn organizational trust prior to deployment. It addresses structured threat-modeling methodology for identifying system vulnerabilities, and hands-on identification and remediation of common web-application security weaknesses. The course extends AI-specific security concerns beyond foundational guardrails into more advanced adversarial techniques, and covers secrets and certificate-management practices for production systems. Compliance posture mapping against recognized regulatory and industry frameworks is covered, along with structured penetration-testing methodology using industry-standard tooling. The course addresses incident-response planning using a recognized framework, including a live simulation component involving detection and coordinated response to an in-progress security incident, followed by communication of security posture and incident outcomes to an executive audience.

This capstone course covers independent application of the full range of program competencies to an entirely new client engagement, under realistic time constraints and without the benefit of an established, continuously evolving system. It addresses client discovery, project scoping, and a complete, compressed full-stack system — spanning data architecture, backend services, a client interface, and an applied AI feature. The course includes structured defense of technical decisions under peer review, a professional technical portfolio and case study, and system-design and behavioral interview scenarios, concluding with a final project demonstration to an external panel of industry evaluators.