Find Your Next Course

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.

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.

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.

No Course Description Available

- 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.

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.

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.

-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

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

No Course Description Available

A good foundation course to introduce basic concepts in the design and analysis of algorithms as well as standard data structures, using Python as a base language for implementing these.

This practical course will teach students to use popular tools for sourcing data, transforming it, analyzing it, communicating these as visual stories, and deploying them in production. Pre-requisites: Python, HTML, JavaScript, Excel, data science basics