NOC:An Introduction to Artificial Intelligence


Lecture 1 - Introduction: What to Expect from AI


Lecture 2 - Introduction: History of AI from 40s - 90s


Lecture 3 - Introduction: History of AI in the 90s


Lecture 4 - Introduction: History of AI in NASA and DARPA (2000s)


Lecture 5 - Introduction: The Present State of AI


Lecture 6 - Introduction: Definition of AI Dictionary Meaning


Lecture 7 - Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally


Lecture 8 - Introduction: Definition of AI Rational Agent View of AI


Lecture 9 - Introduction: Examples Tasks, Phases of AI and Course Plan


Lecture 10 - Uniform Search: Notion of a State


Lecture 11 - Uniformed Search: Search Problem and Examples - Part 2


Lecture 12 - Uniformed Search: Basic Search Strategies - Part 3


Lecture 13 - Uniformed Search: Iterative Deepening DFS - Part 4


Lecture 14 - Uniformed Search: Bidirectional Search - Part 5


Lecture 15 - Informed Search: Best First Search - Part 1


Lecture 16 - Informed Search: Greedy Best First Search and A* Search - Part 2


Lecture 17 - Informed Search: Analysis of A* Algorithm - Part 3


Lecture 18 - Informed Search Proof of optimality of A* - Part 4


Lecture 19 - Informed Search: Iterative Deepening A* and Depth First Branch and Bound - Part 5


Lecture 20 - Informed Search: Admissible Heuristics and Domain Relaxation - Part 6


Lecture 21 - Informed Search: Pattern Database Heuristics - Part 7


Lecture 22 - Local Search: Satisfaction Vs Optimization - Part 1


Lecture 23 - Local Search: The Example of N-Queens - Part 2


Lecture 24 - Local Search: Hill Climbing - Part 3


Lecture 25 - Local Search: Drawbacks of Hill Climbing - Part 4


Lecture 26 - Local Search: of Hill Climbing With random Walk and Random Restart - Part 5


Lecture 27 - Local Search: Hill Climbing With Simulated Anealing - Part 6


Lecture 28 - Local Search: Local Beam Search and Genetic Algorithms - Part 7


Lecture 29 - Adversarial Search: Minimax Algorithm for two player games


Lecture 30 - Adversarial Search: An Example of Minimax Search


Lecture 31 - Adversarial Search: Alpha Beta Pruning


Lecture 32 - Adversarial Search: Analysis of Alpha Beta Pruning


Lecture 33 - Adversarial Search: Analysis of Alpha Beta Pruning (Continued...)


Lecture 34 - Adversarial Search: Horizon Effect, Game Databases and Other Ideas


Lecture 35 - Adversarial Search: Summary and Other Games


Lecture 36 - Constraint Satisfaction Problems: Representation of the atomic state


Lecture 37 - Constraint Satisfaction Problems: Map coloring and other examples of CSP


Lecture 38 - Constraint Satisfaction Problems: Backtracking Search


Lecture 39 - Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search


Lecture 40 - Constraint Satisfaction Problems: Inference for detecting failures early


Lecture 41 - Constraint Satisfaction Problems: Exploiting problem structure


Lecture 42 - Logic in AI : Different Knowledge Representation systems - Part 1


Lecture 43 - Logic in AI : Syntax - Part 2


Lecture 44 - Logic in AI : Semantics - Part 3


Lecture 45 - Logic in AI : Forward Chaining - Part 4


Lecture 46 - Logic in AI : Resolution - Part 5


Lecture 47 - Logic in AI : Reduction to Satisfiability Problems - Part 6


Lecture 48 - Logic in AI : SAT Solvers: DPLL Algorithm - Part 7


Lecture 49 - Logic in AI : Sat Solvers: WalkSAT Algorithm - Part 8


Lecture 50 - Uncertainty in AI: Motivation


Lecture 51 - Uncertainty in AI: Basics of Probability


Lecture 52 - Uncertainty in AI: Conditional Independence and Bayes Rule


Lecture 53 - Bayesian Networks: Syntax


Lecture 54 - Bayesian Networks: Factoriziation


Lecture 55 - Bayesian Networks: Conditional Independences and d-Separation


Lecture 56 - Bayesian Networks: Inference using Variable Elimination


Lecture 57 - Bayesian Networks: Reducing 3-SAT to Bayes Net


Lecture 58 - Bayesian Networks: Rejection Sampling


Lecture 59 - Bayesian Networks: Likelihood Weighting


Lecture 60 - Bayesian Networks: MCMC with Gibbs Sampling


Lecture 61 - Bayesian Networks: Maximum Likelihood Learning


Lecture 62 - Bayesian Networks: Maximum a-Posteriori Learning 


Lecture 63 - Bayesian Networks: Bayesian Learning


Lecture 64 - Bayesian Networks: Structure Learning and Expectation Maximization


Lecture 65 - Introduction, Part 10: Agents and Environments


Lecture 66 - Decision Theory: Steps in Decision Theory


Lecture 67 - Decision Theory: Non Deterministic Uncertainty


Lecture 68 - Probabilistic Uncertainty and Value of perfect information


Lecture 69 - Expected Utility vs Expected Value


Lecture 70 - Markov Decision Processes: Definition


Lecture 71 - Markov Decision Processes: An example of a Policy


Lecture 72 - Markov Decision Processes: Policy Evaluation using system of linear equations


Lecture 73 - Markov Decision Processes: Iterative Policy Evaluation


Lecture 74 - Markov Decision Processes: Value Iteration


Lecture 75 - Markov Decision Processes: Policy Iteration and Applications and Extensions of MDPs


Lecture 76 - Reinforcement Learning: Background


Lecture 77 - Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning)


Lecture 78 - Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning)


Lecture 79 - Reinforcement Learning: TD Learning


Lecture 80 - Reinforcement Learning: TD Learning and Computational Neuroscience


Lecture 81 - Reinforcement Learning: Q Learning


Lecture 82 - Reinforcement Learning: Exploration vs Exploitation Tradeoff


Lecture 83 - Reinforcement Learning: Generalization in RL


Lecture 84 - Deep Learning: Perceptrons and Activation functions


Lecture 85 - Deep Learning: Example of Handwritten digit recognition


Lecture 86 - Deep Learning: Neural Layer as matrix operations


Lecture 87 - Deep Learning: Differentiable loss function


Lecture 88 - Deep Learning: Backpropagation through a computational graph


Lecture 89 - Deep Learning: Thin Deep Vs Fat Shallow Networks


Lecture 90 - Deep Learning: Convolutional Neural Networks


Lecture 91 - Deep Learning: Deep Reinforcement Learning


Lecture 92 - Ethics of AI: Humans vs Robots


Lecture 93 - Ethics of AI: Robustness and Transparency of AI systems


Lecture 94 - Ethics of AI: Data Bias and Fairness of AI systems


Lecture 95 - Ethics of AI: Accountability, privacy and Human-AI interaction


Lecture 96 - Wrapup