INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Semester 6
Course Code BAI654D CIE Marks 50
Teaching Hours/Week (L: T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Examination type (SEE) Theory
Course objectives:
• To understand the primitives of AI
• To familiarize Knowledge Representation Issues
• To understand fundamentals of Statistical Reasoning, Natural Language Processing.
Teaching-Learning Process (General Instructions)
These are sample strategies; which teachers can use to accelerate the attainment of the various
course outcomes.
1. Lecturer method (L) does not mean only the traditional lecture method, but different
types of teaching methods may be adopted to achieve the outcomes.
2. Utilize video/animation films to illustrate the functioning of various concepts.
3. Promote collaborative learning (Group Learning) in the class.
4. Pose at least three HOT (Higher Order Thinking) questions in the class to stimulate
critical thinking.
5. Incorporate Problem-Based Learning (PBL) to foster students' analytical skills and
develop their ability to evaluate, generalize, and analyze information rather than
merely recalling it.
6. Introduce topics through multiple representations.
7. Demonstrate various ways to solve the same problem and encourage students to devise
their own creative solutions.
8. Discuss the real-world applications of every concept to enhance students'
comprehension.
9. Use any of these methods: Chalk and board, Active Learning, Case Studies
Module-1
What is artificial intelligence? Problems, Problem Spaces, and search
Text Book 1: Ch 1, 2
Module-2
Knowledge Representation Issues, Using Predicate Logic, representing knowledge using
Rules.
Text Book 1: Ch 4, 5 and 6.
Module-3
Symbolic Reasoning under Uncertainty, Statistical reasoning
Text Book 1: Ch 7, 8
Module-4
Game Playing, Natural Language Processing
Text Book 1: Ch 12 and 15
Module-5
Learning, Expert Systems.
Text Book 1: Ch 17 and 20
Module-Wise Timeline & Activities
Module 1: Introduction to AI & Problem Solving (Feb 15 – Feb 28, 2025)
Feb 15 – Feb 21: Introduction to AI, history, applications
Feb 22 – Feb 28: Problem spaces, problem formulation, and search strategies
Activities:
Reading: Textbook 1, Chapters 1 & 2
Assignment: Write a short essay on real-world AI applications
Practical: Implement BFS and DFS algorithms
Quiz 1 on Feb 28
Module 2: Knowledge Representation (Mar 1 – Mar 14, 2025)
Mar 1 – Mar 7: Knowledge Representation, Predicate Logic
Mar 8 – Mar 14: Rule-Based Knowledge Representation
Activities:
Reading: Textbook 1, Chapters 4, 5 & 6
Assignment: Represent a real-world scenario using predicate logic
Practical: Implement a simple rule-based system
Quiz 2 on Mar 14
Module 3: Reasoning Under Uncertainty (Mar 15 – Mar 31, 2025)
Mar 15 – Mar 21: Symbolic reasoning under uncertainty
Mar 22 – Mar 31: Statistical reasoning (Bayesian Networks, Probabilistic Models)
Activities:
Reading: Textbook 1, Chapters 7 & 8
Assignment: Solve a reasoning problem using uncertainty models
Practical: Implement Bayesian reasoning in Python
Test on Mar 31 (Modules 1–3)
Module 4: Game Playing & Natural Language Processing (Apr 1 – Apr 20, 2025)
Apr 1 – Apr 10: Game-playing algorithms (Minimax, Alpha-Beta Pruning)
Apr 11 – Apr 20: Basics of Natural Language Processing (NLP)
Activities:
Reading: Textbook 1, Chapters 12 & 15
Assignment: Write an AI-based strategy for a simple game
Practical: Implement Minimax for Tic-Tac-Toe
NLP Task: Implement tokenization and sentiment analysis
Quiz 3 on Apr 20
Module 5: Learning & Expert Systems (Apr 21 – May 10, 2025)
Apr 21 – Apr 30: Basics of Machine Learning
May 1 – May 10: Introduction to Expert Systems
Activities:
Reading: Textbook 1, Chapters 17 & 20
Assignment: Explain the working of an Expert System
Practical: Implement a basic decision tree or rule-based expert system
Quiz 4 on May 10
