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.
1) Podcast
2) Flip class3) PPT presentation4) Research paper publication5) Minor Project6) Assignments : 37) Lab file8)Case studies9) GD10)Blog writing on AI / ML11) Quiz12)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)
Earn a Coursera Certificate based on "AI" or from INDIAAI course (any AI course)
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.
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Assignment-1 [Handwritten]
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Refer AI Question Bank for Practice
Unit-by-Unit PowerPoint content for quick access.
Unit-wise notes for your convenience
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
- Data Collection
- Use a dataset of academic
papers, books, and journals (e.g., from Google Scholar, arXiv, or
institutional repositories).
- 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.
- Tools & Technologies
- Python (scikit-learn, NLTK, spaCy, gensim)
- Database: MySQL / MongoDB
- Graph search: networkx library
- 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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