Department of Computer Science and Engineering (CSE), IIT Bombay

Description:
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:

• Unix and C/C++ programming environment
• Software management tools
• Python, NumPy, and SciPy
• Web programming (HTML, JavaScript, CSS)
• Security and cryptography
• Reliable AI-enabled programming

Description:
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:

• 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

Description:
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:

• 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/authorization 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 (buffer overflow, format-string, race conditions etc) and best practices

Description:
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 different 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:

• 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

Description:
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:

• Database system internals: data storage, indexing, query processing, query optimisation, transactions and concurrency control
• Database system architectures
• Introduction to big data systems
• Parallel and distributed storage
• Parallel and distributed query processing
• Recovery mechanisms
• Parallel and distributed transaction processing

Description:
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 efficiency 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:

• Motivation and applications of blockchain systems
• Introduction to peer-to-peer and distributed systems (Consensus, byzantine fault tolerance, impossibility results)
• Cryptographic tools used in blockchains
• Bitcoin: Proof-of-Work (PoW) consensus, block structure and other details
• Attacks on Bitcoin: double spending and selfish mining
• Energy saving: Proof-of-Stake (PoS) and comparison with Proof-of-Work (PoW)
• Layer-2 Scalability Solutions: Lightning Network and Rollups
• Solidity Smart Contracts for Ethereum

Description:
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:

• Linear algebra methods
• Statistical techniques
• Multivariate calculus
• Machine learning applications
• Automatic face recognition with pose variations
• Optimisation methods for neural networks
• Generating realistic pictures

Description:
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 different 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:

• Machine learning basics, deep learning, and Generative AI concepts
• Single Neuron Computation: Perceptron, Sigmoid neurons
• Feed-Forward Neural Networks (FFNN) and backpropagation, word vectors
• Convolutional Neural Networks (CNN) and image captioning application of CNN
• Recurrent Neural Networks (RNN), Hopfield networks and Boltzmann machines, Backpropagation Through Time (BPTT) and vanishing/exploding gradient
• Long Short-Term Memory (LSTM) networks, Recurrent Convolutional Neural Networks (RcNN), encoder-decoder and decoding methods (autoregression, self-attention, cross-attention)

Description:
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:

• Challenges in vision, image formation, and the camera matrix
• Homographies
• Calibration, stereo
• Image filtering, filter banks
• Convolutional neural networks
• Vision transformers
• Applications in classification, segmentation, inpainting, style transfer, motion, and depth prediction