Details of CS4103 (Autumn 2025)
| Level: 4 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| CS4103 | Artificial Intelligence for Data Science | Monidipa Das |
| Syllabus |
|---|
| INTRODUCTION TO AI:
Artificial Intelligence Introduction, Brief History, Intelligent Agents, Types of agents Python Primer for AI PROBLEM SOLVING BY SEARCH: Problem formulation, Concept of state space search Introduction to Uninformed Search Techniques: Breadth First Search, Depth First Search, Depth First Search with Iterative Deepening, Uniform Cost Search Introduction to Informed/Heuristic search techniques: Greedy Best First Search, A* search, Hill Climbing search, Simulated Annealing search Introduction to GA, GA Operations: Selection, Crossover, Mutation Solving N-Queen Problem using GA Adversarial Search: Minimax algorithm, Alpha-Beta Pruning Building a bot to play tic-tac-toe/Building 8-puzzle solver CONSTRAINT SATISFACTION PROBLEM (CSP): Introduction to CSP, Constraint Graph, Binary and Higher order CSP, Backtracking Search, MRV heuristic, Degree heuristic, Least Constraining-value heuristic, Forward Checking, Arc Consistency, Min-Conflicts Algorithm Solving map coloring problem, Solving puzzle INTRODUCTION TO KNOWLEDGE REPRESENTATION AND LOGIC: Propositional Logic (PL) and Reasoning with PL, Concept of Forward and Backward chaining, First Order Logic (FOL) and Reasoning with FOL, Rule-based systems Parsing family tree using logic/Solving puzzle using logic PROBABILISTIC REASONING: Introduction, Probabilistic reasoning with Bayesian Network Disease diagnosis using Bayesian Network (or other domain specific application) MACHINE LEARNING (ML): Introduction to the concept of learning, k-Nearest Neighbor (k-NN), Decision tree (DT), Naive- Bayes (NB), Support Vector Machine (SVM), Neural Network (NN) Models Building ML models for Steel Plate Fault detection Building ML models for predicting Aquatic Toxicity (or other domain specific application) |
| Prerequisite |
|---|
|
CS1101, or basic knowledge on programming with Python Knowledge on basic data structures and algorithms |
| References |
|---|
| Text Book:
1. Artificial Intelligence A Modern Approach, by S. Russell. Norvig,PHI, Third Edition Reference Books: 1. A First Course in Artificial Intelligence by Deepak Khemani, McGraw Hill Education (India), 2013. 2. Artificial Intelligence by Kevin Knight, Elaine Rich, Third Edition 3. Artificial Intelligence: Foundations of Computational Agents by David L. Poole, Alan K. Mackworth 4. Machine Learning by Mitchell, Tom M., Indian Edition 5. Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Mller, Sarah Guido |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 1 | Not Allowed |
| 2 | IP | 3 | Not Allowed |
| 3 | IP | 5 | Not Allowed |
| 4 | MP | 1 | Not Allowed |
| 5 | MP | 3 | Not Allowed |
| 6 | MR | 1 | Not Allowed |
| 7 | MR | 3 | Not Allowed |
| 8 | MS | 3 | Not Allowed |
| 9 | MS | 5 | Not Allowed |
| 10 | MS | 7 | Elective |
| 11 | MS | 9 | Elective |
| 12 | RS | 1 | Elective |
| 13 | RS | 2 | Elective |