Learn · Artificial Intelligence: Foundations and Algorithms
Lecture 27 of 102Week 4
Local Search: Hill Climbing With Simulated Anealing Part-6
Prof. Mausam · IIT Delhi
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- Artificial Intelligence: Foundations and Algorithms, IIT Delhi (at NPTEL)
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All the lectures
- Introduction: What to Expect from AI
- Introduction: History of AI from 40s - 90s
- Introduction: History of AI in the 90s
- Introduction: History of AI in NASA & DARPA(2000s)
- Introduction: The Present State of AI
- Introduction: Definition of AI Dictionary Meaning
- Introduction: Definition of AI Thinking VS Acting and Humanly VS Rationally
- Introduction: Definition of AI Rational Agent View of AI
- Introduction: Examples Tasks, Phases of AI & Course Plan
- Uniform Search: Notion of a State
- Uniformed Search: Search Problem and Examples Part-2
- Uniformed Search: Basic Search Strategies Part-3
- Uniformed Search: Iterative Deepening DFS Part-4
- Uniformed Search: Bidirectional Search Part-5
- Informed Search: Best First Search Part-1
- Informed Search: Greedy Best First Search and A* Search Part-2
- Informed Search: Analysis of A* Algorithm Part-3
- Informed Search Proof of optimality of A* Part-4
- Informed Search: Iterative Deepening A* and Depth First Branch & Bound Part-5
- Informed Search: Admissible Heuristics and Domain Relaxation Part-6
- Informed Search: Pattern Database Heuristics Part-7
- Local Search: Satisfaction Vs Optimization Part-1
- Local Search: The Example of N-Queens Part-2
- Local Search: Hill Climbing Part-3
- Local Search: Drawbacks of Hill Climbing Part-4
- Local Search: of Hill Climbing With random Walk & Random Restart Part-5
- Local Search: Hill Climbing With Simulated Anealing Part-6
- Local Search: Local Beam Search and Genetic Algorithms Part-7
- Adversarial Search : Minimax Algorithm for two player games
- Adversarial Search : An Example of Minimax Search
- Adversarial Search : Alpha Beta Pruning
- Adversarial Search : Analysis of Alpha Beta Pruning
- Adversarial Search : Cutting Off Search
- Adversarial Search : Horizon Effect, Game Databases & Other Ideas
- Adversarial Search: Summary and Other Games
- Constraint Satisfaction Problems: Representation of the atomic state
- Constraint Satisfaction Problems: Map coloring and other examples of CSP
- Constraint Satisfaction Problems: Backtracking Search
- Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search
- Constraint Satisfaction Problems: Inference for detecting failures early
- Constraint Satisfaction Problems: Exploiting problem structure
- Logic in AI : Different Knowledge Representation systems - Part 1
- Logic in AI : Syntax - Part - 2
- Logic in AI : Semantics - Part - 3
- Logic in AI : Forward Chaining - Part 4
- Logic in AI : Resolution - Part - 5
- Logic in AI : Reduction to Satisfiability Problems - Part - 6
- Logic in AI : SAT Solvers : DPLL Algorithm - Part - 7
- Logic in AI : Sat Solvers: WalkSAT Algorithm - Part - 8
- Uncertainty in AI: Motivation
- Uncertainty in AI: Basics of Probability
- Uncertainty in AI: Conditional Independence & Bayes Rule
- Bayesian Networks: Syntax
- Bayesian Networks: Factoriziation
- Bayesian Networks: Conditional Independences and d-Separation
- Bayesian Networks: Inference using Variable Elimination
- Bayesian Networks: Reducing 3-SAT to Bayes Net
- Constraint Satisfaction Problems: Representation of the atomic state
- Constraint Satisfaction Problems: Map coloring and other examples of CSP
- Constraint Satisfaction Problems: Backtracking Search
- Constraint Satisfaction Problems: Variable and Value Ordering in Backtracking Search
- Constraint Satisfaction Problems: Inference for detecting failures early
- Constraint Satisfaction Problems: Exploiting problem structure
- Bayesian Networks: Rejection Sampling
- Bayesian Networks: Likelihood Weighting
- Bayesian Networks: MCMC with Gibbs Sampling
- Bayesian Networks: Maximum Likelihood Learning
- Bayesian Networks: Maximum a-Posteriori Learning
- Bayesian Networks: Bayesian Learning
- Bayesian Networks: Structure Learning and Expectation Maximization
- Introduction, Part 10: Agents and Environments
- Decision Theory: Steps in Decision Theory
- Decision Theory: Non Deterministic Uncertainty
- Decision Theory: Probabilistic Uncertainty & Value of perfect information
- Decision Theory: Expected Utility vs Expected Value
- Markov Decision Processes: Definition
- Markov Decision Processes: An example of a Policy
- Markov Decision Processes: Policy Evaluation using system of linear equations
- Markov Decision Processes: Iterative Policy Evaluation
- Markov Decision Processes: Value Iteration
- Markov Decision Processes: Policy Iteration and Applications & Extensions of MDPs
- Reinforcement Learning: Background
- Reinforcement Learning: Model-based Learning for policy evaluation (Passive Learning)
- Reinforcement Learning: Model-free Learning for policy evaluation (Passive Learning)
- Reinforcement Learning: TD Learning
- Reinforcement Learning: TD Learning and Computational Neuroscience
- Reinforcement Learning: Q Learning
- Reinforcement Learning: Exploration vs Exploitation Tradeoff
- Reinforcement Learning: Generalization in RL
- Deep Learning : Perceptrons and Activation functions
- Deep Learning : Example of Handwritten digit recognition
- Deep Learning : Neural Layer as matrix operations
- Deep Learning : Differentiable loss function
- Deep Learning : Backpropagation through a computational graph
- Deep Learning : Thin Deep Vs Fat Shallow Networks
- Deep Learning : Convolutional Neural Networks
- Deep Learning : Deep Reinforcement Learning
- Ethics of AI : Humans vs Robots
- Ethics of AI : Robustness and Transparency of AI systems
- Ethics of AI : Data Bias and Fairness of AI systems
- Ethics of AI : Accountability, privacy and Human-AI interaction
- Wrapup