Published 1/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Basic ideas and techniques in the design of intelligent computer systems.
What you’ll learn
Identify potential areas of applications of AI
Basic ideas and techniques in the design of intelligent computer systems
Statistical and decision-theoretic modeling paradigm
How to build agents that exhibit reasoning and learning
Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
Requirements
The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge of statistics and mathematics is an added advantage to take up this Machine learning course
Description
Overview
Lecture 3 Intelligent Agents
Lecture 4 Information on State Space Search
Lecture 5 Graph theory on state space search
Lecture 6 Solution for State Space Search
Lecture 7 FSM
Lecture 8 BFS on Graph
Lecture 9 DFS algo
Lecture 10 DFS with iterative deepening
Lecture 11 Backtracking algo
Lecture 14 Summary_state space search
Lecture 15 Heuristic search overview
Lecture 18 Simple hill climbing
Lecture 19 Best first search algo
Lecture 20 Tracing best first search-1
Lecture 21 Best first search continue
Lecture 22 Admissibility-1
Lecture 23 Mini-max
Lecture 24 Two ply min max
Lecture 25 Alpha beta pruning
Lecture 26 Machine learning_overview
Lecture 27 Perceptron learning
Lecture 28 Perceptron with linearly separable
Lecture 29 Backpropagation with multilayer neuron
Lecture 30 W for hidden node and backpropagation algo
Lecture 31 Backpropagation algorithm explained
Lecture 34 Updation of weight and cluster
Lecture 35 K-Means cluster‚NNalgo and appliaction of machine learning
Lecture 38 Propotional calculus
Lecture 39 Predicate calculus
Lecture 40 First order predicate calculus
Lecture 41 modus ponus,tollens
Lecture 42 Unification and deduction process
Lecture 43 Resolution refutation
Lecture 44 Resolution refutation in detail
Lecture 45 Resolution refutation example-2 convert into clause
Lecture 46 Resoultion refutation example-2 apply refutation
Lecture 47 Unification substitution andskolemization
Lecture 49 Model based and CBR reasoning
Lecture 50 Production system
Lecture 51 Trace of production system
Lecture 52 Knight tour prob in chessboard
Lecture 55 Goal driven Vs data driven and inserting and removing facts
Lecture 56 Defining rules and commands
Lecture 57 CLIPS installation and clipstutorial 1
Lecture 58 CLIPS tutorial 2
Lecture 59 CLIPS tutorial 3
Lecture 60 CLIPS tutorial 4
Lecture 63 Tutorial 6
Lecture 64 CLIPS tutorial 7
Lecture 65 CLIPS tutorial 8
Lecture 67 Tutorial 10
Lecture 74 Not operator
Lecture 77 Truth and control
Lecture 78 Tutorial 12
Lecture 79 Intelligent agent
Lecture 80 Simple reflex agent
Lecture 81 Simple reflex agent with internal state
Lecture 82 Goal based agent
Lecture 83 Utility based agent
Lecture 84 Basics of utility theory
Lecture 85 Maximum expected utility
Lecture 86 Decision theory and decision network
Lecture 87 Reinforcement learning
Lecture 88 MDPand DDN
Lecture 91 Probability distribution
Lecture 92 Baysian rule for conditional probability
Lecture 93 Examples of Bayes Theorm
