Curriculum
Section 1: Overview of Artificial Intelligence
1 Introduction to Artificial Intelligence
2 Definition of Artificial Intelligence
3 Intelligent Agents
Section 2:Representation and Search : State Space Search
4 Information on State Space Search
5 Graph theory on state space search
6 Problem Solving through state space search
7 DFS algo
8 DFS with iterative deepening
9 backtracking algo
10 trace backtracking on graph part_1
11 trace backtracking on graph part_2
12 summary_state space search
Section 3: Representation and search : Heuristic search
13 Heuristic search overview
14 heuristic calculation technique part _1
15 heuristic calculation technique part _2
16 simple hill climbing
17 best first search algo
18 tracing best first search-1
19 best first search continue
20 admissibility-1
21 mini-max
22 two ply min max
23 alpha beta pruning
Section 4: Machine Learning
24 machine learning_overview
25 perceptron learning
26 perceptron with linearly separable
27 backpropagation with multilayer neuron
28 W for hidden node and backpropagation algo
29 backpropagation algorithm explained
30 backpropagation calculation_part01
31 backpropagation calculation_part02
32 updation of weight and cluster
33 k-means cluster,NNalgo and appliaction of machine learning
Section 5:Logics and reasoning
34 logics_reasoning_overview_propositional calculas part 1
35 logics_reasoning_overview_propositional calculas part 2
36 propotional calculus
37 predicate calculus
38 First order predicate calculus
39 modus ponus,tollens
40 unification and deduction process
41 resolution refutation
42 resolution refutation in detail
43 resolution refutation example-2 convert into clause
44 resoultion refutation example-2 apply refutation
45 unification substitution andskolemization
46 prolog overview_some part of reasoning
47 model based and CBR reasoning
Section 6: Rule based Programming
48 production system
49 trace of production system
50 knight tour prob in chessboard
51 Goal driven_data driven production system part _ 1
52 Goal driven_data driven production system part _ 2
53 goal driven Vs data driven and inserting and removing facts
54 defining rules and commands
55 CLIPS installation and clipstutorial1
56 CLIPS tutorial 2
57 CLIPS tutorial 3
58 CLIPS tutorial 4
59 CLIPS tutorial 5_part01
60 CLIPS tutorial 5_part02
61 tutorial 6
62 CLIPS tutorial 7
63 CLIPS tutorial 8
64 variable in pattern tutorial 9
65 tutorial 10
66 more on wildcardmatching_part01
67 more on wildcardmatching_part02
68 more on variables
69 deffacts and deftemplates_part01
70 deffacts and deftemplates_part02
71 template indetail part1
72 not operator
73 forall and exists_part01
74 forall and exists_part02
75 truth and control
76 tutorial 12
Section 7: Decision Making
77 intelligent agent
78 simple reflex agent
79 simple reflex agent with internal state
80 goal based agent
81 utility based agent
82 basics of utility theory
83 maximum expected utility
84 decision theory and decision network
85 reinforcement learning
86 MDPand DDN
Section 8: Stochastic methods
87 basics of set theory part _1
88 basics of set theory part _2
89 probability distribution
90 baysian rule for conditional probability
91 examples of bayes theorm
