DS203: Programming for Data Science | by Dr. Vinay Kulkarni

• Probability and statistics fundamentals
• Data exploration and visualization
• Regression and supervised learning
• Introduction to deep learning
• Databases, cloud services, and parallel processing

CS768: Learning with Graphs | by Prof. Abir De

• Network science and generative graph models
• Link prediction and diffusion processes
• Representation learning on graphs
• Graph neural networks
• Deep generative models for graphs

IE616: Decision Analysis and Game Theory | by Prof. Veeraruna Kavitha

• Decision making under risk and uncertainty
• Utility theory and Bayesian inference
• Dynamic programming and optimization
• Classical and modern game theory
• Nash equilibrium and evolutionary models

ME781: Statistical ML and Data Mining | by Prof. Asim Tewari, IITB

• Essentials of statistics and data mining stages
• Methods overview: clustering, classification, regression, sequence analysis
• Applications: process control, scheduling, quality control
• Introduction to cyber-physical systems and Big Data analytics

CS663: Digital Image Processing | by Prof. Ajit Rajwade, IITB

• Image enhancement: histogram equalization, smoothing, sharpening
• Edge detection and PCA for face recognition
• Image restoration: denoising and deblurring
• Image compression: JPEG, wavelets
• Segmentation: region-based methods, Hough transform

CS347: Operating Systems | by Prof. Mythili Vutukuru, IITB

• Types of systems: batch, multiprogramming, real-time
• Process management: synchronization, deadlocks
• Memory management: paging, segmentation
• File systems and security
• Case study: UNIX

GNR638: ML in Remote Sensing | by Prof. Biplab Banerjee, IITB

• Ad-hoc feature extraction and image classification
• Deep CNNs: AlexNet, ResNet, VGGNet, etc.
• Image segmentation using CNNs like U-Net
• Domain adaptation and transfer learning
• Generative models: GANs, Cycle-GANs

CS769: Optimization for ML | by Prof. Ganesh Ramakrishnan, IITB

• Continuous optimization: gradient methods, convexity, duality
• Discrete optimization: combinatorial problems, submodular functions
• Applications: feature selection, data summarization
• Advanced topics: distributed algorithms, non-convex optimization

CS626: NLP, Speech and the Web | by Prof. Pushpak Bhattacharyya, IITB

• Speech processing: articulation, HMMs
• Morphology: inflectional, agglutinative paradigms
• Parsing theories: HPSG, X-Bar, Minimalism
• Applications: sentiment analysis, multilingual QA

CS419: Introduction to ML | by Prof. Abir De, IITB

• Supervised learning: SVMs, naive Bayes, decision trees
• Unsupervised learning: k-means, hierarchical clustering
• Applications: image recognition, text retrieval

CS218: Design and Analysis of Algorithms | by Prof. Abhiram Ranade, IITB

• Algorithm design techniques: greedy, dynamic programming
• Complexity classes: P, NP, NP-complete problems
• Approximation algorithms for NP-hard problems

CS719: Topics in Mathematical Foundations of Formal Verification | by Prof. Bharat Adsul, IITB

• Logics like First Order Logic, Monadic Second Order Logic, Linear Temporal Logic and their corresponding Automata theories
• Semigroup, Monoids and Set Theory to inference an automata from a monoid divided into equivalence classes each of which correspond to a state.
• Various Theorems like Green's theorem in order to build upon this theory giving rise to a lot of interesting characteristics.

CS213: Data Structure and Algorithms | by Prof. Milind Sohoni, IITB

• Studied Stacks, Queue, Deque, Dictionaries, Hashing, Trees, AVL, Strings, Priority Queues, Binary Heap, Graphs and other Data Structures and their applications.

CS228: Logic for Computer Science | by Prof. Krishna S., IITB

• Preposition logic and Proof Rules along with Normal Forms, Holm Algorithm and Resolution
• First Order Logic, Monadic Second Order Logic, Linear Temporal Logic and Proving their Satisfiability and Regular-ness
• Automatas including DFA and NFA and the languages accepted by them. Also studied Büchi Automata
• Proving closure of automata under complementation using smart use of 2-player games and alternating Automata

CS101: Computer Programming and Utilization | by Prof. Parag Chaudhuri, IITB

• Basics of programming in C++, starting with variables, condtional statements, loops
• Learn about arrays, strings and other data types
• Pointers, Functions, Classes and Objects in order to grasp a basic knowledge of programming

Introduction to Cybersecurity | by CyberSecurity Club, IITB

• Basics of hacking: Python, Linux, C and bit of Assembly
• Cryptography: Classical and Modern Crypto inlcuding Advanced Encryption Standard and Diffie-Hellman Key Exchange
• Forensics: Stenanography, Image Forensics and Network Forensics

Winter in Data Science | by Analytics Club, IITB

• Leant the basics of Data Science: Exploratory Data Analysis, Supervised Learing Algorithms
• Model making, Regression, Tree and Forest Classification
• Natural Language Processing and Neural Networks