Course Overview

Available Courses

1. CS-4101: Artificial Intelligence

View course notes → — Complete lecture series on classical and modern AI methods — state-space search ($A^*$, Minimax, MCTS), Constraint Satisfaction Problems (CSPs), Rule-based & Fuzzy Expert Systems, Propositional & First-Order Logic, Decision Theory, MDPs, Reinforcement Learning, and Bayes Nets.

2. CS-4125: Machine Learning

View course notes → — Comprehensive guide to supervised and unsupervised learning algorithms — linear & logistic regression, gradient descent, neural network representations & backpropagation, SVMs, $k$-means clustering, PCA, anomaly detection, recommender systems, and 2024 Final Exam Solutions.

3. CS-4101: AI Previous Year Questions & Solutions

View AI PYQs → — Detailed exam solutions and problem analyses for CS-4101 AI assessments.

4. Statistical Mechanics for Deep Learning & CS

View course notes → — A rigorous curriculum bridging statistical physics, information theory, and generative AI / LLMs (Energy-Based Models, Free Energy, DPO, Infinite-Width NTK, Spin Glasses & Attention, Phase Transitions & Grokking, Langevin Dynamics & Diffusion Models, Flow Matching).