VTU Online Class - Course Details

"An Introduction to Artificial Intelligence"

Mausam D

5.0

(1) 206 Students

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What you will learn

Artificial Intelligence

Machine Learning

The course introduces the variety of concepts in the field of artificial intelligence. It discusses the philosophy of AI, and how to model a new problem as an AI problem. It describes a variety of models such as search, logic, Bayes nets, and MDPs, which can be used to model a new problem. It also teaches many first algorithms to solve each formulation. The course prepares a student to take a variety of focused, advanced courses in various subfields of AI.

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: Greedy Best First Search and A* Search Part-2

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 : Analysis of Alpha Beta Pruning (contd...)

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

Bayesian Networks: Rejection Sampling

Bayesian Networks: Likelihood Weighting

Bayesian Networks: MCMC with Gibbs Sampling

Bayesian Networks: Maximum Likelihood Learning

Bayesian Networks: Maximum a-Posteriori LearningÂ

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

Probabilistic Uncertainty & Value of perfect information

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

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

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Course Duration

31 h 34 m 0 s
Course Level

Intermediate
Student Enrolled

206
Language

English

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31 h 34 m 0 s Video Lectures
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