Lectures
You can download the lectures here (in PDF format). I will try to upload lectures prior to their corresponding classes.

Introduction to Artificial Intelligence
tl;dr: What is AI? How does it impact our lives? The current state of the art.
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Introduction to Search
tl;dr: How to formulate intelligent behaviour as search problems?
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Informed Search and Heuristics
tl;dr: How to perform informed search with the help of heuristics?
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Constraint Satisfaction Problems and Iterative Improvement
tl;dr: Introduction to CSPs and how to solve them (backtracking, filtering, arc consistency, etc.); Also, iterative improvement (hill climbing, simulated annealing, genetic algorithms, etc.)
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Game  Adversarial Search
tl;dr: What if you have an opponent? Zerosum games, minimax, evaluation functions and alphabeta pruning.
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Game  Expectimax Search
tl;dr: What if the game is not fully predictable? i.e., there is stochasticity in the process.
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Markov Decision Processes
tl;dr: How to find optimal policy in an MDP? You'll also learn about following algorithms: Value Iteration, Policy Evaluation, and Policy Iteration (all based on Bellman updated equations).
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Reinforcement Learning
tl;dr: How to learn while playing? Reinforcement Learning, Qlearning, exploration vs. exploitation, featurebased state representation.
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Introduction to Deep Learning*
tl;dr: What is deep learning? How is it different from machine learning? What is data representation?
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Mathematics of Neural Networks*
tl;dr: What are building blocks of networks, i.e. tensors? How to train a network with gradient descent and backpropagation?
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Getting Started with Neural Networks*
tl;dr: Getting a taste of deep learning with three examples: sentiment analysis, text classification, and regression.
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Introduction to Convolutional Neural Networks*
tl;dr: A very brief introduction to computer vision with ConvNets.
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Introduction to Recurrent Neural Networks*
tl;dr: A very brief introduction to RNNs, specifically Long ShortTerm Memory (LSTM).
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