Degree Courses

This course aims to give students a comprehensive understanding of Machine Learning Operations (MLOps). MLOps is a paradigm to deploy and maintain machine learning models in production environments reliably and efficiently. The course will cover various aspects of MLOps, including model development, model deployment, monitoring, and optimization. Students will gain hands-on experience with popular MLOps tools and frameworks, enabling them to manage machine learning workflows effectively to deliver robust and scalable ML solutions.

-To learn the workings of markets through the prism of Demand and Supply. -Understand how a rational consumer makes any choice. -Learn the relationships between production, costs, and profits -Learn different forms of markets and their key characteristics. -Understand how governments regulate businesses. -Learn the business model behind the success of Amazon, UBER, Alibaba, iPhone

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We look at how an intelligent agent solves new problems. Starting with blind search we quickly move on to heuristic search, and look at several variations designed to combat the combinatorial explosion that search has to face. We study how board games like Chess and Go are played; how search facilitates logical reasoning; and approaches to domain independent planning of actions to achieve a goal. We end with looking at an alternative formulation that combines search and reasoning as constraint processing.

To prepare the students to understand the phases of testing based on requirements for a project, to apply the concepts taught in the course to formulate test requirements precisely, to design and execute test cases as a part of a standard software development IDE, and to apply specially designed test case design techniques for specific application domains.

To study the basics of Neural Networks and their various variants such as the Convolutional Neural Networks and Recurrent Neural Networks, to study the different ways in which they can be used to solve problems in various domains such as Computer Vision, Speech and NLP.

To enable the student to use the creative process to identify and solve problems in an effective way, to use structured creative thinking tools to investigate a particular matter from a variety of perspectives with clarity, to communicate and share thoughts/information accurately and effectively to understand each other, to become a team player, to value other cultures, to overcome obstacles she/he may face when performing a task, to work with hands to become better at engaging and enhancing their thought process, to get pertinent information crucial for learning about something or for communicating, to understand how decisions at the individual level or at the business level affect optimal utilisation of resources, to use conflict resolution tools to effectively resolve conflicts, to perceive emotions of themselves and of others and manage for a better outcome under various circumstances.

An introduction to Finance and Accounting, The life cycle of a financial transaction, Areas where AI/ML is used in the Finance Industry, Importance of Model Explainability in the Regulated World, An introduction to solving real world finance problems – Credit Card Fraud Detection, Identity Fraud Detection, Anti Money Laundering Scenarios

At the end of the course, students will be able to understand the differences between the various sequential decision making problems based on the type of feedback involved, recognize practical ML problems as sequential decision making problems whenever they are, learn about optimal algorithms for several sequential decision making settings, and apply the algorithms studied in the course to various practical sequential decision making scenarios.

-Knowledge of basics of image processing and computer vision -Knowledge of building blocks of deep learning including feedforward networks, convolutional neural networks, recurrent neural networks and transformers -Knowledge of generative AI models in computer vision -Knowledge of recent trends including explainability/zero-shot learning, few-shot learning, self-supervised learning, etc -Hands-on experience on implementation of basic image processing tasks -Hands-on experience on implementation of deep learning models for computer vision tasks -Hands-on experience on implementation of advanced computer vision tasks such as explainability, self-supervised learning, etc

- Learn how to think of social and economic aspects of life via mathematical models. - Learn how game theory is applied to think about problems in information economy.

-Recognise the full stack of deep learning - datasets, frameworks, hardware for training, deployment across devices, interpretability, and security -Use tools to improve deep learning practice throughout the entire stack -Apply best practices in training and deployment, even under constraints of data and hardware -Build confidence of training models of real-world scale -Identify problems of social relevance that are solvable with deep learning

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The aim of this second-level graduate course is to provide a broad overview and develop the tools and methods necessary for the large-scale problems that naturally arise in many data science-related application areas.

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To provide a basic understanding of research methodology and its implementation in different business domains, to understand the role, scope, process, cost, and value of marketing research, to match research techniques to marketing problems, to analyse data and translate them into actionable findings, to enable students to do hands-on research to solve business problems.

To learn about the internal organisation of the computer. To learn about the architecture of a computer’s CPU.

This experiential course immerses you in an empathy-led, data-driven approach to designing products and services through Design Thinking, guiding you to identify and analyze real user needs and swiftly create functional mockups with visual building tools—ideal for learners exploring product management, venture creation, design and innovation, and looking to address real business needs with human-centered solutions.

To provide students with the foundations necessary for understanding and extending the current state of the art in data visualization, to gain an understanding of the key techniques and theory used in visualization, including data models, graphical perception and techniques for visual encoding and interaction, to plan for data-based storytelling through charts, maps, and diagrams, to use visualization tools to transform quantitative information to visual representation, and to gain practical experience building and evaluating visualizations.

To enable the students to “understand” biological data, to represent, and analyze various datasets from a network perspective, to encourage network thinking applied to problems across disciplines, to understand various network models used to model real-world networks, to apply network analytics techniques to understand biological networks, to implement basic network analysis algorithms in Python, to learn different AI/ML problem formulations for biological data, and to apply AI/ML techniques for analysis of biological data using Python.

To introduce computational methods involved in statistical estimation and learning problems.

This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Students will learn about various types of deep generative models such as variational autoencoders, generative adversarial networks, autoregressive models, Diffusion Models and Large Language Models. The course will cover both theoretical foundations and practical implementations of these models using popular frameworks like PyTorch. Students will gain hands-on experience through lectures and assignments, allowing them to explore deep generative models across various AI tasks.

This course will introduce students to practical aspects of analytics at a large scale, i.e. big data. The course will start with a basic introduction to big data and cloud concepts spanning hardware, systems and software, and then delve into the details of algorithm design and execution at large scale.

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To enable the student to understand the reinforcement learning paradigm, to be able to identify when an RL formulation is appropriate, to understand the basic solution approaches in RL, to implement and evaluate various RL algorithms.

To introduce advanced ideas in design of algorithms; To study the performance guarantees of algorithms; To introduce methods for coping with NP-hard problems.

To introduce linear statistical models and their applications in estimation and testing. The course will illustrate concepts with specific examples, data sets and numerical exercises using statistical package R.

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Understanding the Transformer architecture Understanding the concept of pretraining and fine-tuning language models Compare and contrast different types of tokenizers like BPE, wordpiece, sentencepiece Understanding different LLMs architectures: encoder-decoder, encoder-only, decoder-only Exploring common datasets like C4,mc4,Pile, Stack and so on Addressing the challenges of applying vanilla attention mechanisms for long range context windows. Apply different types of fine-tuning techniques to fine-tune large language models

This course is intended as a practical introduction to C programming. The focus is on gaining experience with writing and debugging programs. At the end of this course, a student should be able to: -write, compile, and run programs in C -use debugging tools to find and correct errors in programs -use various constructs in C and the standard library of C to implement basic data structures and algorithms -understand the need for an OS and how programs interact with the system

To prepare students to develop an algorithmic thinking to address key data science challenges in bioinformatics, to acquire knowledge of various problem formulations and algorithm paradigms, which have transformed the field of biomedicine in modern times, to obtain insights into many key bioinformatics algorithms on strings, trees, and graphs, many of which can be applied to other areas as well.

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