My Table

Unit 1

S No.	Course Content:
0	Artificial Intelligence
1	(Introduction to AI): Definitions
2	Goals of AI
3	AI Approaches
4	AI Techniques
5	Branches of AI
6	Applications of AI
7	Introduction of Intelligent Systems: Agents and Environments
8	Good Behavior: the concept of Rationality
9	The Nature of Environments
10	The structure of Agents
11	How the components of agent programs work

Unit 2

S No.	Course Content:
0	(Problems Solving, Search and Control Strategies)
1	Solving Problems by Searching
2	Study and analysis of various searching algorithms
3	Implementation of Depth-first search
4	Problem-Solving Agents
5	Searching for Solutions
6	Uninformed Search Strategies: Breadth-first search
7	Uniform-cost search
8	Depth-first search
9	Depth-limited search
10	Iterative deepening depth-first search
11	Bi-directional search
12	Informed (Heuristic) Search Strategies: Greedy best-first search
13	A* search: Minimizing the total estimated solution cost
14	Conditions for optimality: Admissibility and consistency
15	Optimality of A*
12	Memory-bounded heuristic search
13	Heuristic Functions
14	Generating admissible heuristics from sub problems: Pattern databases
15	Learning heuristics from experience
16	Beyond Classical Search: Local Search
17	Algorithms and Optimization Problems: Hill-climbing search Simulated annealing
18	Local beam search
19	Genetic algorithms
21	Local Search in Continuous Spaces
22	Searching with Non-deterministic Actions: AND-OR search trees
23	Searching with Partial Observations

S No.	Course Content:
1	Acting under Uncertainty
2	Basic Probability Notation
3	Inference Using Full Joint Distributions
4	Bayes' Rule and Its Use
5	Representing Knowledge in an Uncertain Domain
6	Other Approaches to Uncertain Reasoning
7	Rule-based methods for uncertain reasoning
10	Representing vagueness: Fuzzy sets and fuzzy logic
11	Study of fuzzy logic and Decision trees
12	Implementation aspects of Decision trees
13	Learning from Examples: Forms of Learning
14	Supervised Learning,
15	Learning Decision Trees
16	The decision tree representation
17	Expressiveness of decision trees
18	inducing decision trees from examples

S No.	Course Content:
1	Introduction
2	Knowledge acquisition
3	Knowledge base
4	Working memory
5	Inference engine
6	Expert system shells
7	Explanation
8	Application of expert systems
9	Fundamentals of Neural Networks: Introduction and research history
10	Model of artificial neuron
11	Characteristics of neural networks
12	learning methods in neural networks
13	Single-layer neural network system
14	Applications of neural networks
15	Fundamentals of Genetic Algorithms: Introduction
16	Encoding, Operators of genetic algorithm
17	Basic genetic algorithm
18	Operators of genetic algorithm