Unit 1 Introduction

4 lecture hours

• (Scope of the Course) Introduction to AI, foundation of AI and history of AI intelligence
• Applications of AI, Agents: Agents and Environments
• the concept of rationality
• the nature of environments, and structure of agents

Unit 2 Searching & Game Playing

8 lecture hours

• Searching for solutions
• uniformed search strategies – Breadth first search
• depth first Search
• Search with partial information (Heuristic search) Hill climbing
• AO* Algorithms
• Problem reduction
• Game Playing- Adversial search
• mini-max algorithm
• optimal decisions in multiplayer games
• Problem in Game playing
• Alpha-Beta pruning
• Evaluation functions

Unit 3 Knowledge Representation & Reasoning

• Knowledge Representations and Mappings
• Approaches To Knowledge Representation
• Representing Knowledge using Rules: Procedural Versus Declarative Knowledge
• Logic Programming
• Forward Versus Backward Reasoning
• Symbolic Reasoning Under Uncertainty and Statistical Reasoning: Introduction to Non-Monotonic Reasoning
• Logics for Nonmonotonic Reasoning Statistical Reasoning: Probability 10 Semester: VII Subject Name: Fundamentals of Artificial Intelligence Subject Code: 3174601 w.e.f. 2022-23 http://syllabus.gtu.ac.in/ Page no. 2 of 4 and Bays’ Theorem
• Certainty Factors and Rule- Base Systems
• Bayesian Networks
• Dempster-Shafer Theory

Unit 4 Fuzzy Logic

8 lecture hours

• Conventional set vs Fuzzy set, Operations of fuzzy set, Membership function
• Fuzzy rules
• Fuzzy inference
• Application for control

Unit 5 Basics of Machine Learning

9 lecture hours

• Types of Learning (supervised learning
• unsupervised learning, and reinforcement learning)
• Hypothesis Space
• Inductive Bias
• Evaluation and Cross Validation
• Linear Regression
• Decision Trees
• Learning Decision Trees
• K-nearest Neighbour
• Collaborative Filtering
• Methods to remove over-fitting problem

Unit 6 Artificial Neural Networks

8 lecture hours

• Biological Neurons and Biological Neural Networks
• Artificial Neural Networks
• Activation Functions
• Perceptron NN, Multilayer Perceptron NN, Back-propagation Neural Networks
• Training Methods

Unit 7 Natural Language Processing

5 lecture hours

• Syntactic Processing
• Semantic Analysis
• Discourse and Pragmatic Processing
• Text Analytics
• Text pre- processing
• Bag of Words
• Word Cloud
• Machine Translation
• sentiment analysis