Start Date:: 26-July-2024
Course Code: CS 401
Course Name: Artificial Intelligence
Semester: Seventh Semester (Core) - B.Tech. (CSE)
L-T-P-C: 3-1-0-4
Course Faculty: Dr. Partha Pakray
Course Plan
| Course Plan | ||||
| UNIT | Descriptions | Lecture Hours | Week | CO |
|
Unit-1 |
Introduction: Introduction and techniques of AI, Importance of AI | 3 | Week 1 | CO-1 |
| Agents and rationality, task environments, agent architecture, Application of AI. | 3 | Week 2 | CO-1 | |
|
Unit-2 |
Search strategies: Search space, Uninformed Search technique, |
3 |
Week 3 |
CO-2 |
| Bread First Search, Depth First search, Informed Search, Heuristic Search technique, constraint satisfaction problems, stochastic search methods, | CO-2 | |||
| Hill climbing, backtracking, graph search, A* algorithm, monotone restriction, production systems, | 3 | Week 4 | CO-2 | |
| AO* algorithm |
3 |
Week 5 |
CO-2 | |
| Searching game trees: MINIMAX procedure, alpha-beta pruning. | CO-2 | |||
|
Unit-3 |
Knowledge representation: Knowledge representation and reasoning, | 3 | Week 6 | CO-2 |
| Propositional logic, First Order logic, Situation calculus, and backward chaining. | 3 | Week 7 | CO-2 | |
| Theorem Proving in First Order Logic, Resolution Tree | 3 | Week 8 | CO-2 | |
| Theorem Proving in First Order Logic, Resolution Tree |
3 |
Week 9 |
CO-2 | |
| STRIPS robot problem solving system, Structured representations of knowledge (Semantic Nets, Frames, Scripts), Rule based representations, forward | CO-3 | |||
|
Unit-4 |
Uncertain Knowledge and Reasoning: Non monotonic & monotonic reasoning | 3 | Week 10 | CO-2 |
| Confidence factors, Bayes theorem, |
3 |
Week 11 |
CO-2 | |
| Dempster & Shafers Theory of evidence, Probabilistic inference, Fuzzy reasoning | CO-2 | |||
|
Unit-5 |
Application: AI in Natural Language Processing and Understanding, | 3 | Week 12 | CO-3 |
| Ecommerce, E-tourism, Industry, Healthcare, vision and Robotics | 3 | Week 13 | CO-3 | |
| Discussion | 1 | Week 14 | ||
| Total | 40 | |||
Course Outcome (CO):
After completion of this course, the students are expected to
1. Student will demonstrate knowledge of the building blocks of AI.
2. Ability to apply Artificial Intelligence techniques for problem solving.
3. Student will participate in the design of systems by applying knowledge representation, reasoning techniques to real-world problems that act intelligently and learn from AI experience.
Topic Covered:
UNIT 1: Introduction
UNIT 2: Search Strategies
UNIT 5: Application: AI in Natural Language Processing and Understanding
Unit-3: Knowledge representation and reasoning
Class PPTs and Notes
In this section you will get all the day-to-day slides.Try Yourself! - No need to submit
Write the names:
|
Task Environment |
Observable |
Deterministic |
Episodic |
Static |
discrete |
Agents |
|
Crossword puzzle |
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Chess with a clock |
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Poker |
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Taxi driving |
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Medical Diagnosis |
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Image Analysis |
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Interactive Tutoring system |
Tutorials:
|
Exp. No. |
Description |
Reference |
|
1 |
Search Engine - Apache Nutch |
https://archive.apache.org/dist/nutch/nutch-0.9.tar.gz |
|
2 |
Language Model |
https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/ |
|
3 |
ChatBot - RASA Framework |
https://rasa.com/, https://huggingface.co/models |
|
4 |
Question Answering - Hugging Face |
https://huggingface.co/models |
Assignments:
Attendance
Will be shared in Google Excel Sheet Soon.Course Evaluation:
Course Feedback:
Previous Year Question Papers
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