| Start Date: | 2025-01-10 | Course Code: | CS 5109 | L-T-P-C: | 3-0-0-3 |
|---|---|---|---|---|---|
| Course Name: | Artificial Intelligence | Semester: | 2nd Semester | Course Faculty: | Partha Pakray |
Course Plan
Course Code: CS 5109
Course Name: Artificial Intelligence
Semester: Second Semester (Core) - M.Tech. (CSE)
L-T-P-C: 3-0-0-3
Start Date: January 10, 2025
Course Faculty: Dr. Partha Pakray
Textbooks
- Artificial Intelligence - Rich, Knight (TMH)
- Principles of Artificial Intelligence - Nilson N. J. (Narosa)
- Paradigms of AI Programming - Norvig P. (Elsevier)
- Introduction to Expert System - Jackson P. (Addison-Wesley)
Course Plan/ Lecture Plan
qu| Course Plan | ||||
| UNIT | Descriptions | Lecture Hours | Week | CO |
|
Unit-1 |
Introduction: Introduction and techniques of AI, Importance of AI | 3 | Week 1 | CO-1 |
| Agents and rationality, task environments, agent architecture, Application of AI. | 3 | Week 2 | CO-1 | |
|
Unit-2 |
Search strategies: Search space, Uninformed Search technique, |
3 |
Week 3 |
CO-2 |
| Bread First Search, Depth First search, Informed Search, Heuristic Search technique, constraint satisfaction problems, stochastic search methods, | CO-2 | |||
| Hill climbing, backtracking, graph search, A* algorithm, monotone restriction, production systems, | 3 | Week 4 | CO-2 | |
| AO* algorithm |
3 |
Week 5 |
CO-2 | |
| Searching game trees: MINIMAX procedure, alpha-beta pruning. | CO-2 | |||
|
Unit-3 |
Knowledge representation: Knowledge representation and reasoning, | 3 | Week 6 | CO-2 |
| Propositional logic, First Order logic, Situation calculus, and backward chaining. | 3 | Week 7 | CO-2 | |
| Theorem Proving in First Order Logic, Resolution Tree | 3 | Week 8 | CO-2 | |
| Theorem Proving in First Order Logic, Resolution Tree |
3 |
Week 9 |
CO-2 | |
| STRIPS robot problem solving system, Structured representations of knowledge (Semantic Nets, Frames, Scripts), Rule based representations, forward | CO-3 | |||
|
Unit-4 |
Uncertain Knowledge and Reasoning: Non monotonic & monotonic reasoning | 3 | Week 10 | CO-2 |
| Confidence factors, Bayes theorem, |
3 |
Week 11 |
CO-2 | |
| Dempster & Shafers Theory of evidence, Probabilistic inference, Fuzzy reasoning | CO-2 | |||
|
Unit-5 |
Application: AI in Natural Language Processing and Understanding, | 3 | Week 12 | CO-3 |
| Ecommerce, E-tourism, Industry, Healthcare, vision and Robotics | 3 | Week 13 | CO-3 | |
| Discussion | 1 | Week 14 | ||
| Total | 40 | |||
Course Outcomes
By the end of this course, students will:
- Demonstrate knowledge of the building blocks of AI.
- Apply AI techniques for problem-solving.
- Participate in designing systems that use AI for intelligent behavior and learning.
Topic Coverage
UNIT 1: Introduction
- Introduction to Artificial Intelligence
- What is AI?
- What is the need of AI?
- History of AI.
- Example of various AI System
- What is Intelligence?
- What is intelligent behaviour?
- Can Machine Think? how.
- What is Turing test?
- Suppose you design a machine to pass the Turing test. What are the capabilities such a machine must have?
- Typical AI Problems
- Easy and Hard Task of AI
- Various Approaches
- What is Deep Blue?
- About Agent and Environment
- Importance of Agents
- Various Agent Architecture
- Types of Agent
- Intelligent Agent
- What is a rational agent ?
- What is bounded rationality ?
- What is an autonomous agent ?
- Describe the salient features of an agent.
- Components of AI.
- Different task domains of AI.
- Why to study Agent?
- Why to study Environment?
UNIT 2: Search Strategies
- Search Problems
- State Space Search
- Example: the 8-puzzle
- Example: 8-queens
- Search Tree
- The basic search algorithm
- Evaluating Search Strategies
- Problem Solving
- Problem representation
- Successor function
- Problem solution
- Problem description
- Uninformed vs. informed search
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Uniform Cost Search (UCS)
- Bidirectional Search
- Informed Search
- Best First Search - Greedy Search
- A* Search
- AO* Search: AND-OR Graph
- Hill Climbig Search
UNIT 5: Application: AI in Natural Language Processing and Understanding
- Introduction to Natural Language Processing
- Various Level of Natural Language Processing
- Ambiguities at Various level at NLP
- Introduction to Language Model
- Future Words Prediction by Language Model
Unit-3: Knowledge representation and reasoning
- Introduction to Knowledge Representation
- Propositional Logic
- First Order Logic
- Practice Session on First Order Logic
Class PPTs and Notes
In this section you will get all the day-to-day slides.- Lecture: Introduction to AI - Introduction to AI - Lecture 1 || Introduction to AI - Lecture 2
- Lecture: Expert System - Expert System
- Lecture: Agent and Enviroment - PPT
- Lecture: Agent Classifications - PPT
- Lecture: Environment Classifications - PPT
- Problem Solving through AI - PPT
- Lecture: Problem Solving using Search (Single agent search) - PPT
- Lecture: Problem Solving using Search - (Single agent search) Uninformed Search - PPT
- Lecture: Uniform-Cost Search Algorithm - Example
- Lecture: Informed Search - PPT
- Lecture: AO* Algorithm - PPT
- Lecture: Hill Climbing - Local Search Example
- Lecture: Natural Language Processing - PPT
- Lecture: Language Model - PPT
- Lecture: Language Model with Smooting - PPT
- Lecture: Knowledge Representation - Propositional Logic - PPT
- Lecture: First Order Logic - PPT
- Lecture: Try This! Click Here
- Lecture: Logic and Resolution - FOL - PPT Resolution Examples
- Lecture: Semantic Nets, Frames, Conceptual Graphs PPT
- Lecture: Constraint Satisfaction Problems (CSPs) Class Notes & PPTs
- - PPT
