Course Code: CS 5202

Course Name: Artificial Intelligence

Semester: 1st Semester, MTech AI (Core)

L-T-P-C: 3-0-0-3

Course Faculty: Dr. Partha Pakray

Course Plan

Course Plan

UNIT	Descriptions	Lecture Hours	Week	CO
UNIT-1	Introduction: Introduction and techniques of AI, Importance of AI	3	Week 1	CO-1
UNIT-1	Agents and rationality, task environments, agent architecture, Application of AI.	3	Week 2	CO-1
UNIT-1	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,	3	Week 3	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.	3	Week 5	CO-2
UNIT-1	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	3	Week 9	CO-3
UNIT-2	Baye’s theorem, multiple features, decision boundaries, estimation of error rates,	3	Week 10	CO-2
	histogram, kernels,	3	Week 11	CO-2
	window estimaters, nearest neighbour	3	Week 11	CO-2
	classification, maximum distance pattern classifier,	3	Week 12	CO-3
	adaptive decision boundaries.	3	Week 13	CO-3
Discussion		1	Week 14
Total		40

Course Outcome (CO):

After completion of this course, the students are expected to

To introduce basic concepts of AI with its working principle.
To understand different kinds of knowledge representations techniques to solve AI problems.
To understand different kinds of heuristic search algorithms to get feasible solution for AI problems.
To design decision making models to solve different problems.

Class PPTs and Notes

In this section you will get all the day-to-day slides.

Lecture: Introduction to AI - PPT
Lecture: Agent and Enviroment - PPT
Lecture: Agent Classifications - PPT
Lecture: Environment Classifications - 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

Try Yourself! - No need to submit

“Surely computers cannot be intelligent; they can do only what their programmers tell them”. Is the latter statement true, and does it imply the former?
For Flipkart shopping agent, what is the Agent Type, Environment, Actuators, Performance Measure, Sensors, Agent Architecture? Assignment
Suppose you want to develop an automated taxi, so what will be the Agent Type, Environment, Actuators, Performance Measure, Sensors?

Write the names:

Task Environment	Observable	Deterministic	Episodic	Static	discrete	Agents
Crossword puzzle
Chess with a clock
Poker
Taxi driving
Medical Diagnosis
Image Analysis
Interactive Tutoring system

Tutorials:

Exp. No.	Description	Reference
1	Search Engine - Apache Nutch	https://archive.apache.org/dist/nutch/nutch-0.9.tar.gz
2	Language Model	https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
3	ChatBot - RASA Framework	https://rasa.com/, https://huggingface.co/models
4	Question Answering - Hugging Face	https://huggingface.co/models

Assignments:

Course Evaluation:

Question Paper - MTech (AI) and MTech (CSE):