Master AI with BCA 303T

 


LEARNING OBJECTIVES:

In this course, the learners will be able to develop expertise related to the following:

1. To learn the basics of designing intelligent agents that can solve general purpose problems.

2. To represent and process knowledge, plan and act, reason under uncertainty and can learn from experiences.





UNIT–I

No. of Hours: 12 Chapter/Book Reference: TB1 [Chapters - 1, 2, 3]; TB2 [Chapters- 1, 3, 4]
Overview of AI: Introduction to AI, Importance of AI, AI and its related field, AI techniques,
Problem solving agents, Criteria for success.
Problems, problem space and search: Defining the problem as a state space search, Depth First Search, Breadth First search, Production Systems and its characteristics, Issues in the design of the search programs.
Heuristic search techniques: Generate and test, hill climbing, best first search technique, A*, AO*, problem reduction, constraint satisfaction.

UNIT–II

No. of Hours: 12 Chapter/Book Reference: TB1 [Chapters - 5, 6]; TB2 [Chapters - 7, 8, 9,
10] RB1 [Chapters - 5, 6, 7]
Knowledge Representation: Definition and importance of knowledge, Knowledge representation, various approaches used in knowledge representation, Issues in knowledge representation, Semantic net frame.
Logical Reasoning: Logical agents, propositional logic, inferences, Syntax and semantics of First Order Logic, Inference in First Order Logic Knowledge Base, forward chaining, backward chaining, unification, resolution

UNIT–III

No. of Hours: 10 Chapter/Book Reference: TB1 [Chapters - 7, 8, 15]; TB2 [Chapters - 13,
14]
Handling Uncertainty: Non-Monotonic Reasoning, Probabilistic reasoning, Bayes ‘Theorem, Certainty factors and Rule-based Systems, Bayesian Networks, Dempster-Shafer Theory, Introduction to Fuzzy logic. Fuzzy set definition & types. Membership functions. Designing a fuzzy set for a given application
Natural Language Processing: Introduction, Syntactic Processing, Semantic Processing, Pragmatic Processing.

UNIT–IV

No. of Hours: 10 Chapter/Book Reference: TB1 [Chapter 17]; TB2 [Chapters - 18, 19]
Learning: Introduction to Learning, Rote Learning, learning by taking advice, learning in problem solving, learning from examples: Induction, Explanation-based Learning, Discovery, Analogy, Neural Networks, and Genetic Learning.
Expert System: Introduction to expert System, Case study of Expert system (Prolog/any programming Language).


TEXT BOOKS:

TB1. Rich and Knight, “Artificial Intelligence”, Tata McGraw Hill, 1992.
TB2. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, Second Edition (Indian reprint: Pearson Education)

REFERENCE BOOKS:

RB1. George F.Luger Artificial Intelligence Pearson Education
RB2. Ben Coppin Artificial Intelligence Illuminated Jones and Bartlett Publisher




BT levels” most commonly refers to Bloom’s Taxonomy levels—a framework used in education to classify learning objectives by complexity. Bloom’s Taxonomy is a widely used educational framework that classifies learning objectives into levels of increasing complexity. It was originally developed by Benjamin Bloom in 1956 and later revised in 2001 to better reflect active thinking and modern teaching practices.



Example : 



CLASS Framework for  AI- BCA-303 
1) Podcast-based learning
2) Flip class 
3) PPT presentation 
4) Research paper publication 
5) Minor Project 
6) Assignments : 3 
7) Lab file 
8)Case studies just click here 
9) Group Discussion (GD)
10)Blog writing on AI / ML
11) Quiz 
12)AI tool comparison (compare different AI tools for the same task) like chatgpt, perplexity, copilot, claude etc.
13)Journal/article review (summarize and critique a recent AI paper)
14)Peer teaching (students teach one topic to the class)
15) Prompt engineering exercises (for generative AI)
16)Model implementation challenge (implement a given algorithm) 
17)Debugging challenge (find and fix errors in ML code) 
18)Ethics debate (e.g., "Should AI replace human decision-making?")
19) GitHub portfolio submission (upload code and documentation) 
20) Research proposal writing (identify a problem and propose an AI solution)  


Internal Exam : October Mid  

Practical Exam  : November End

Final Exam : December Starting

 

 Earn a Coursera Certificate based on "AI"  or from INDIAAI course (any AI course)


Free AI Courses


[Kaggle Learn](https://www.kaggle.com/learn?utm_source=chatgpt.com)


[fast.ai](https://course.fast.ai?utm_source=chatgpt.com)


[DeepLearning.AI](https://www.deeplearning.ai?utm_source=chatgpt.com)


[NPTEL](https://nptel.ac.in?utm_source=chatgpt.com)


[Coursera](https://www.coursera.org?utm_source=chatgpt.com) (audit for free)



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Assignment-1 [Handwritten]


Assignment -2 


Assignment-3 


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Refer AI Question Bank for Practice


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Explore : https://indiaai.gov.in/

INDIAai (https://indiaai.gov.in/) is the Government of India’s official portal for Artificial Intelligence, created by the Ministry of Electronics and Information Technology (MeitY) to build a national AI ecosystem. It serves as a central hub for AI innovation, education, research, and policy development in India.

Overview of INDIAai

  • Launched by: Ministry of Electronics and Information Technology (MeitY)
  • Purpose: To democratize AI access, foster innovation, and promote ethical, inclusive AI development across India.
  • Vision: Position India as a global leader in responsible and scalable AI adoption.

🌍 Major Initiatives

Global IndiaAI Summit (2024, New Delhi): Focused on compute capacity, foundational models, datasets, and ethical AI.

India–AI Impact Summit 2026: Announced by Prime Minister Narendra Modi; first global AI summit hosted in the Global South (Feb 19–20, 2026).

Global Partnership on AI (GPAI): India’s collaboration with international AI research and policy networks.

📚 Resources Available on the Portal

News & Articles: Latest AI developments and government initiatives.

Case Studies & Research Reports: Indian and global AI applications.

Startups Directory: Profiles of emerging AI ventures.

Events Calendar: Summits, workshops, and hackathons.

Educational Materials: AI learning modules and FutureSkills programs.









Unit-by-Unit PowerPoint content for quick access. 

Unit-1 

Unit-2 

Unit-3

Unit-4 


Unit-wise notes for your convenience 

Unit-1

Unit-2

Unit-3

Unit-4


Explore https://course.fast.ai/Resources/kaggle.html


Links For Coding & AI Implementation 


[Google Colab](https://colab.research.google.com?utm_source=chatgpt.com) Python, free GPU, notebooks for All AI practicals 

[Kaggle](https://www.kaggle.com?utm_source=chatgpt.com) Datasets, competitions, notebooks ML, data preprocessing, projects 

[Hugging Face](https://huggingface.co?utm_source=chatgpt.com) NLP models, LLMs, datasets NLP, Transformers

[Scikit-learn Documentation](https://scikit-learn.org?utm_source=chatgpt.com) Machine Learning algorithms Classification & clustering

[TensorFlow](https://www.tensorflow.org?utm_source=chatgpt.com) Deep Learning Advanced AI

[PyTorch](https://pytorch.org?utm_source=chatgpt.com) Research and AI development Deep Learning


Links for Robotics & Simulation


[Roboflow](https://roboflow.com?utm_source=chatgpt.com) – Computer vision projects with Colab integration. 


[OpenCV](https://opencv.org?utm_source=chatgpt.com) – Image processing.


[ROS (Robot Operating System)](https://www.ros.org?utm_source=chatgpt.com) – Robotics programming.


[NVIDIA Isaac Lab](https://developer.nvidia.com/isaac-lab?utm_source=chatgpt.com) – Robot simulation.



Physical Visits (Delhi NCR)

  1. National Science Centre – Robotics exhibits, AI demonstrations, STEM workshops.
  2. Indian Institute of Technology Delhi – Robotics and AI research labs (through prior permission).
  3. Indraprastha Institute of Information Technology Delhi – AI and robotics labs, seminars, and technical events. Their robotics club, CYBORG, regularly conducts hands-on activities. 
  4. Atal Tinkering Labs – Many schools and innovation hubs host robotics demonstrations and maker activities.




Each student to:


1. Create a GitHub portfolio.

2. Complete 10 Kaggle micro-courses.

3. Build 5 Google Colab notebooks.

4. Participate in one Kaggle competition.

5. Fine-tune one Hugging Face NLP model.

6. Develop one end-to-end AI mini-project.

7. Visit one AI/robotics lab or science centre and submit a reflection report.

8. Present one recent AI research paper.



 What AI does  

1. Understand: Read text, images, voice 
2. Reason: Solve problems, plan steps
3. Create: Write, code, design, make images/video
4. Act: Use tools, search web, send emails, book meetings






                                            2. Types of AI you use daily



3. How an AI Agent works  
Goal → Think → Use Tools → Learn from Result → Repeat  

Ex: "Find me 3 IT jobs in Delhi" → Searches → Filters → Summarizes → Sends you


4. What you can build with AI right now

1. Personal agent: Email writer, meeting scheduler, researcher

2. Business agent: Customer support, resume screener, content creator  

3. Creative agent: Image generator, video editor, music maker

Best AI Tools for 2026

  1. Work: Meeting notes, emails, research, data analysis 
  2. Study: Tutor, flashcards, paper summarizer 
  3. Content: Image gen, video, voice cloning, social posts





1. For WORK 💼




2. For STUDY 📚



3. For CONTENT 🎨






Top 3 "Must-Have" Stack for 2026

Meta AI: Daily assistant + images + brainstorming
NotebookLM: For study and research with your own files
Canva + HeyGen: For all content and videos















Case Study Framework

Explore more case studies here also 

Title: AI‑Powered Library Search System for Academic Resources

1. Problem Statement

Students and faculty often struggle to locate relevant academic resources (books, research papers, journals) in large digital libraries. Traditional keyword‑based search fails to capture context, leading to irrelevant or incomplete results.

Challenge: How can AI‑driven search techniques improve accuracy, relevance, and user satisfaction in academic resource retrieval?

2. Objectives

  • Implement a smart search engine using AI techniques.
  • Compare keyword‑based search vs semantic search.
  • Demonstrate how heuristic search algorithms (A*, BFS, DFS) can optimize query handling.
  • Evaluate system performance using metrics like precision, recall, and response time.

3. Methodology

  1. Data Collection
    • Use a dataset of academic papers, books, and journals (e.g., from Google Scholar, arXiv, or institutional repositories).
  2. System Design
    • Phase 1: Implement keyword‑based search (TF‑IDF, inverted index).
    • Phase 2: Implement semantic search using NLP + embeddings (Word2Vec, BERT).
    • Phase 3: Apply heuristic search algorithms to optimize query expansion and ranking.
  3. Tools & Technologies
    • Python (scikit-learn, NLTK, spaCy, gensim)
    • Database: MySQL / MongoDB
    • Graph search: networkx library
  4. Evaluation
    • Compare keyword vs semantic search results.
    • Measure accuracy (precision/recall), efficiency (response time), and user satisfaction (survey/feedback).

4. Expected Outcomes

  • Semantic search provides more relevant results than keyword search.
  • Heuristic search improves efficiency in query handling.
  • Students and faculty experience faster, smarter resource discovery.
  • Case study demonstrates practical application of AI + search techniques in education.

5. Deliverables

  • Working prototype of the AI‑powered search system.
  • Comparative analysis report (keyword vs semantic search).
  • Presentation with flowcharts, algorithm explanation, and performance metrics.

📖 Case Study 2

Title: AI‑Driven Job Portal Recommendation Engine

1. Problem Statement

Job seekers often face difficulty finding suitable opportunities due to generic keyword matching. Employers also struggle to identify the right candidates quickly.

Challenge: How can AI search techniques improve job‑candidate matching efficiency?

2. Objectives

  • Implement a recommendation engine for job portals.
  • Compare rule‑based search vs AI‑driven semantic search.
  • Use heuristic search to rank candidates/jobs by relevance.

3. Methodology

  • Dataset: Job listings + candidate resumes (sample datasets or synthetic data).
  • Techniques:
    • Keyword search (TF‑IDF).
    • Semantic search (embeddings, cosine similarity).
    • Heuristic ranking (A* search for best fit).
  • Tools: Python, scikit‑learn, spaCy, gensim.

4. Expected Outcomes

  • Improved candidate‑job matching accuracy.
  • Faster retrieval compared to traditional search.
  • Demonstrated use of AI in recruitment systems.

📖 Case Study 3

Title: Smart Campus Navigation Using AI Search Algorithms

1. Problem Statement

Large campuses confuse new students and visitors. Traditional maps are static and don’t adapt to real‑time changes (blocked paths, events).

Challenge: How can AI search techniques optimize navigation inside a campus?

2. Objectives

  • Design a smart navigation system for campus routes.
  • Apply graph search algorithms (Dijkstra, A*, BFS).
  • Integrate real‑time updates (blocked paths, shortest routes).

3. Methodology

  • Dataset: Campus map converted into a graph (nodes = locations, edges = paths).
  • Techniques:
    • BFS/DFS for basic pathfinding.
    • Dijkstra/A* for shortest path with heuristics.
  • Tools: Python, networkx, Google Maps API (optional).

4. Expected Outcomes

  • Efficient route suggestions for students/faculty.
  • Demonstrated application of AI search in real‑world navigation.
  • Prototype mobile/web app for campus use.

📖 Case Study 4

Title: AI‑Powered Medical Diagnosis Support System

1. Problem Statement

Doctors often need quick decision support for diagnosis. Traditional systems rely on static rules and don’t adapt well to complex symptom combinations.

Challenge: How can AI search techniques assist in faster, more accurate diagnosis?

2. Objectives

  • Build a decision support system for medical diagnosis.
  • Compare rule‑based search vs heuristic search.
  • Use AI to suggest possible conditions based on symptoms.

3. Methodology

  • Dataset: Public medical datasets (e.g., symptom‑disease mappings).
  • Techniques:
    • Decision trees for rule‑based diagnosis.
    • Heuristic search for narrowing down possibilities.
    • NLP for symptom input.
  • Tools: Python, scikit‑learn, NLTK.

4. Expected Outcomes

  • Faster diagnosis suggestions.
  • Demonstrated efficiency of heuristic search in medical decision support.
  • Prototype system usable for academic demonstration.




 



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