Artificial Intelligence

Paper Code: 
25CBCA403
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

1.   Understand the basics of artificial intelligence and its subfields.

2.   Explore real-world applications of AI across different industries.

3.   Gain insights into the ethical, social, and economic implications of AI.

4.   Develop an appreciation for the potential of AI to drive innovation and                          transformation

 

Course Outcomes: 

Course

Learning

Outcomes

(at course level)

Learning and teaching

strategies

Assessment

Strategies

Course

Code

Course

Title

25CBCA403

 

Artificial Intelligence (Theory)

CO211.Identify the scope of AI techniques in different application domains.

CO212. Investigate AI search techniques, game-playing strategies, and knowledge representation.

CO213.Apply supervised, unsupervised, and reinforcement learning in real-world scenarios.

CO214.  Identify real- world applications of AI across various industries.

CO215. Analyse the ethical, social, and economic implications of AI.

CO216. Contribute effectively in course- specific interaction.

Approach in teaching:Interactive Lectures, Discussion, Reading assignments, Demonstration.

 

Learning activities for the students: Self learning assignments, Seminar presentation.

Class test, Semester end examinations, Quiz, Assignments, Presentation.

 

9.00
Unit I: 

Introduction to Artificial Intelligence

Foundation and scope of Artificial Intelligence (AI), Historical overview and key milestones, AI vs. Human intelligence, Generic Applications of AI, General problem solving, production system and control strategy, Water Jug Problem.

 

9.00
Unit II: 

Role and significance of searching in AI, State space search in AI, Blind/Uninformed Search: BFS and DFS, Concept of heuristic search technique: Generate and Test, BFS, Introduction to Game Playing: Tic-tac-toe, Knowledge representation techniques (Semantic Nets, Scripts) and inference mechanism.

 

9.00
Unit III: 

AI Subfields and Technologies

Basics of Machine learning: Supervised, unsupervised, and reinforcement learning, Neural networks and Deep learning, Natural language processing (Syntactic and Semantic) and Computer vision.

 

9.00
Unit IV: 

Applications of AI

Introduction to Expert Systems, AI in healthcare: Diagnosis, treatment, and medical imaging, AI in finance: Fraud detection, algorithmic trading, and risk assessment, AI in transportation: Autonomous vehicles and traffic optimization, Generative AI:   AI in customer service and catboats, AI in education: Personalized learning and intelligent tutoring systems

9.00
Unit V: 

Ethical and Social Implications of AI

Ethical guidelines and responsible AI practices, Privacy and data protection concerns, Impact of AI on employment and the workforce, AI and Innovation, Emerging trends and future directions in AI, AI and creativity: Artistic applications.

 

ESSENTIAL READINGS: 

ESSENTIAL READINGS:

1. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”,                Prentice Hall of India, 1st edition, 2015.

2. Vinod Chandra S. S & Anand Hareendran S., “Artificial Intelligence and Machine            Learning” , PHI, 2022.

3. Kevin Warwick , “Artificial Intelligence: The Basics” Routledge, 2011.

4. Subhasree Bhattacharjee, “Artificial Intelligence for Student” Shroff Publishers and        Distributors Pvt.LTD., 1st  Edition, 2016

 

REFERENCES: 

SUGGESTED READINGS:

1. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill, 3rd                edition, 2009.

2. Nils  J.  Nilsson,  “Principles  of  Artificial  Intelligence  (Symbolic  Computation  /      Artificial Intelligence)”, reprint edition, 2014.

3. Stuart Russell, Peter Norving, “Artificial Intelligence: A Modern Approach”,                Pearson Education, 3rd edition, 2010.

 

e-RESOURCES:

1. AI for Everyone, By Coursera, https://www.coursera.org/learn/ai-for-everyone

2. AI Fundamentals for non-Data Scientist, By Coursera,                            https://www.coursera.org/learn/wharton-ai-fundamentals-non-data-scientists

3. Artificial Intelligence, By Swayam,                                                                       https://onlinecourses.swayam2.ac.in/cec21_cs08/preview

4. An Introduction to Artificial Intelligence,                                                                https://onlinecourses.nptel.ac.in/noc22_cs56/preview

 

JOURNALS:

1. Artificial Intelligence An International Journal, Elsevier,                                            https://www.journals.elsevier.com/artificial-intelligence

2. Journal of Artificial Intelligence, ScienceDirect,                                                      https://www.sciencedirect.com/journal/artificial-intelligence

3. Journal of Artificial Intelligence, Asian Network for Scientific                                    Information, https://www.ansinet.com/guideline.php?issn=1994-5450

4. International Journal of Artificial Intelligence and Soft Computing, Inderscience,         https://www.inderscience.com/jhome.php?jcode=ijaisc

 

Academic Year: 
2025-26
  • Printer-friendly version