IIT Bombay ePGD in Computer Science and AI for Tech Professionals

In this course, you will learn mathematical techniques that are essential in solving problems traditionally considered challenging for computational machines. You will gain a strong understanding of key methods in linear algebra, statistics and multivariate calculus that are widely used in machine learning. By the end of the course, you will be equipped to apply these mathematical concepts to real-world tasks such as automatic face recognition with pose variations, optimising neural networks and generating realistic images. Topics Covered • Linear Algebra • Statistical Techniques • Multivariate Calculus • Machine Learning Applications • Face Recognition • Neural Network Optimization • Image Generation

This course will equip you with a comprehensive understanding of how agents make optimal decisions under uncertainty. Starting with multi-armed bandits and Markov Decision Processes (MDPs), you will understand problem abstraction, real-world applications, and core solution techniques. The course then develops expertise in value-based methods, including their extension using function approximation and deep learning for complex, high-dimensional environments. It further covers dierent classes of RL methods such as policy-gradient and actor–critic algorithms, along with trust-region approaches that stabilise learning. You will also explore multi-agent RL settings, understand recent advances in gradient-based RL, and study policy search methods and decision-time planning, ultimately gaining both theoretical foundations and practical tools to design and analyse advanced RL systems. Topics Covered • Multi-Armed Bandits • Markov Decision Processes (MDPs) • Value-Based Reinforcement Learning • Function Approximation • Deep Reinforcement Learning • Policy Gradient Methods • Actor-Critic Algorithms • Trust Region Methods • Multi-Agent Reinforcement Learning • Policy Search • Decision-Time Planning

In this course, you will learn the fundamental concepts and challenges in computer vision, starting with image formation and the camera matrix. You will study the techniques of homographies and calibration, stereo vision, image filtering, filter banks, as well as convolutional neural networks (CNNs) and visual transformers. You will also explore various applications of computer vision, such as classification, segmentation, inpainting, style transfer, motion analysis and depth prediction. Topics Covered • Image Formation & Camera Matrix • Homographies • Calibration & Stereo Vision • Image Filtering • Convolutional Neural Networks (CNNs) • Vision Transformers • Image Classification • Segmentation • Inpainting • Style Transfer • Motion Analysis • Depth Prediction

Generative AI and AI agents are rapidly transforming software, knowledge work, and decision-making across industries. This course equips you with end-to-end practical capabilities, from understanding the mathematical foundations of machine learning and Neural Networks to exploring the inner workings of transformer-based Large Language Models and building LLM applications featuring Retrieval-Augmented Generation (RAG), tool calling (MCP and skills), chain-of-thought reasoning, multi-agent orchestration, memory, security, and evaluation. Topics Covered • Machine Learning Fundamentals • Neural Networks • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • AI Agent Tool Calling • AI Agent Reasoning • Agent Orchestration • Memory • Security • AI Evaluation

In this course, you will learn the foundational concepts of machine learning, deep learning and Generative AI. The course will cover Feed-Forward Neural Networks (FFNN) backpropagation techniques, as well as applications of word vectors in Natural Language Processing. You will explore dierent kinds of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and learn how they are built, trained and used. You will also study LSTM networks, RCNN, Encoder-Decoder architectures, with a focus on autoregression, self-attention and cross-attention mechanisms used in Large Language Models. Topics Covered • Feed-Forward Neural Networks • Backpropagation • Word Vectors • Convolutional Neural Networks • Recurrent Neural Networks • LSTM Networks • Recurrent Neural Networks (RNN) • Encoder–Decoder Architecture • Autoregression • Self-Attention • Cross-Attention

In this course, you will be introduced to essential tools and programming environments for software development. You will learn the Unix operating system, the C/C++ programming environment, and various software management tools. The course will cover the concepts behind Python programming, its popular libraries such as NumPy and SciPy, Web programming with HTML, CSS and JavaScript, and elements of AI programming. To solidify your understanding, you will complete a course project that demonstrates your grasp of the key concepts covered in the course. Topics Covered • Unix • C/C++ Programming • Software Management Tools • Python • NumPy • SciPy • HTML • CSS • JavaScript • Security & Cryptography • Reliable AI-Enabled Programming

In this course, you will explore foundational concepts of algorithms and computational complexity. You will learn fundamental techniques for solving computational problems like induction, recursion, divide and conquer, dynamic programming and greedy algorithms. You will gain a strong understanding of complexity theory, by studying concepts of undecidability, polynomial-time problems, complexity classes, NP-hardness and NP-completeness. Topics Covered • Induction and recursion, divide and conquer • Dynamic programming • Greedy algorithms • Bipartite matching • Network flow and problem reductions • Undecidability, polynomial-time complexity • Complexity classes NP and co-NP, NP-hardness and NP-completeness

This course provides a comprehensive overview of web and software security, focusing on key concepts such as web protocols, session management and server internals. You will explore both server-side and client-side vulnerabilities, and also software and OS security, including the fundamentals of Linux security. You will also learn the use of tools and frameworks like OWASP Top 10 vulnerabilities, CVE database and CVSS scoring, integral to understanding and mitigating cybersecurity risks. Topics Covered • Web background (protocols, session management, server/browser internals etc) • Web security tools (Firefox developer tools and OWASP ZAP) • Server-side web attacks (featuring in OWASP top 10; SSRF, SQL injection, authentication/authorisation vulnerabilities etc) • Client-side web attacks (featuring in OWASP top 10; XSS, CSRF, CORS, Web sockets etc) • Web security landscape and overall defense strategies • Basics of Linux security • Software based attacks (buer overflow, format-string, race conditions etc) and best practices

In this course, you will learn both cryptography and network security, starting with an overview of confidentiality, crypto-analysis, data integrity, and cryptographic protocols. You will explore various network attacks across dierent layers of the protocol stack, such as Eavesdropping, ARP spoofing and DHCP attacks. The course also covers secure network protocols, firewalls and intrusion detection systems, providing you with the knowledge to secure and defend modern network infrastructures against potential threats. Topics Covered • Confidentiality primitives: symmetric-key and asymmetric key encryption • Integrity primitives: hashes; Message Authentication Codes (MAC) and digital signatures • Cryptographic protocols: key distribution and public key infrastructure, human and cryptographic authentication • Case study of TLS protocol • Overview of computer networks • Attacks at various layers of the network protocol stack (e.g. MAC flooding, ARP spoofing, DHCP/DNS attacks including DOS) • Secure network protocols (IPsec, DNSSEC) • Firewalls and intrusion detection systems

In this course, you will explore the internals of database systems, covering key concepts such as data storage, indexing, query processing and transaction management. You will learn database system architectures, the internals of big data systems and the challenges of parallel and distributed storage and query processing. The course will provide a strong foundation in building and managing real-world database systems through hands-on assignments with open-source databases and big data systems. Topics Covered • Data Storage • Indexing • Query Processing • Query Optimization • Transactions • Concurrency Control • Database Architecture • Big Data Systems • Distributed Storage • Parallel Query Processing • Recovery Mechanisms

In this course, you will explore the motivation and real-world applications of blockchain systems. You will gain an understanding of peer-to-peer and distributed systems, and their core concepts such as consensus mechanisms, Byzantine fault tolerance and impossibility results. The course will also introduce cryptographic tools essential for the functioning of blockchains. You will study Bitcoin, its Proof-of-Work consensus and potential attacks like double spending and selfish mining. You will also examine energy eciency in blockchain, comparing Proof of Stake with Proof of Work consensus models. You will also be introduced to layer-2 scalability solutions such as Lightning Network and Rollups. You will develop smart contracts in Solidity for Ethereum and test them on your personal Ethereum blockchain. Topics Covered • Blockchain Applications • Distributed Systems • Consensus Mechanisms • Byzantine Fault Tolerance • Bitcoin • Proof of Work • Proof of Stake • Double Spending • Lightning Network • Rollups • Solidity • Ethereum