NOC:An Introduction to Artificial Intelligence (USB)

Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. Mausam

NPTEL Co-ordinating Institute : IIT Delhi

NPTEL Lecture Count : 96

NPTEL Course Size : 24 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)

Lecture Titles:

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 LearningÂ
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