π Syllabus (CSE472 - Theory)
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Unit 1: INTRODUCTION TO AI
- A. Foundation of AI, Goals of AI, History and AI course line
- B. Introduction to Intelligent Agents; Environment; Structure of Agent
- C. AI Solutions Vs Conventional Solutions; a philosophical approach; a practical approach
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Unit 2: PROBLEM SOLVING AGENTS
- A. Problem solving using Search Techniques; Problems; Solutions; Optimality
- B. Informed Search Strategies; Greedy Best-First; A* Search; Heuristic Functions
- C. Uninformed Search Strategies; BFS; DFS; DLS; UCS; IDFS; BDS
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Unit 3: KNOWLEDGE & REASONING
- A. Knowledge-Based Agents; Logic; First-Order Logic; Syntax-Semantics in FOL; Simple usage
- B. Inference Procedure; Inference in FOL; Reduction; Inference Rules
- C. Forward Chaining; Backward Chaining; Resolution
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Unit 4: LEARNING
- A. Common Sense Vs Learning; Components; Representations; Feedback
- B. Learning Types: Supervised; Unsupervised; Reinforcement Learning
- C. Artificial Neural Networks: Introduction, types of networks; Single Layer and Multi-Layer Networks
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Unit 5: APPLICATIONS
- A. AI Present & Future; application case studies on NLP, Image Processing
- B. Robotics β Hardware; Vision; Navigation based case studies
- C. Ambient Intelligence case studies
Syllabus (CSP472 - Lab)
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Unit 1: Practical based on goal-based problems with LISP
- 1. Introduction to Lisp, basic programming structures, and sample programs related to AI.
- 2. Write a LISP function to compute the sum of squares.
- 3. Write a LISP function to compute the difference of squares.
- 4. Write a Recursive LISP function that returns the last element of a list.
- 5. Write a Recursive LISP function that returns a list except the last element.
- 6. Write a Recursive LISP function that returns the reverse of a list.
- 7. Write a Recursive LISP function that removes the first occurrence of an atom from a list.
- 8. Write a Recursive LISP function that appends two lists.
- 9. Write a Recursive LISP function that merges two lists alternately.
- 10. Write a function that computes factorial of a number.
- 11. Write a function that evaluates a fully parenthesized infix expression.
- 12. Write a function that performs DFS traversal of a binary tree.
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Unit 2: Practical related to Uninformed Search Algorithms
- 1. Write a LISP/Python program for the Water Jug Problem.
- 2. Write a LISP/Python program that determines if an integer is prime.
- 3. Implement BFS and DFS on a graph in Python.
- 4. Implement Uniform Cost Search on weighted graphs.
- 5. Simulate Depth-Limited and Iterative Deepening Search.
- 6. Apply Greedy BFS with heuristics.
- 7. Implement A* search algorithm with heuristics.
- 8. Solve Sudoku using backtracking and forward-checking.
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Unit 3: AGENT, KNOWLEDGE & REASONING
- 1. Simulate a logic-based agent for the Wumpus World.
- 2. Generate truth tables and evaluate logic expressions.
- 3. Create inference engines using chaining techniques.
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Unit 4: APPLICATIONS
- 1. Build and visualize decision trees using sklearn.
- 2. Train a logistic regression model on a dataset.
- 3. Implement K-Nearest Neighbors classifier.
- 4. Use ensemble methods for classification.
- 5. Build a simple neural network in NumPy.
- 6. Train MLP using TensorFlow/Keras.
- 7. Train CNN on MNIST/CIFAR-10 using Keras.
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Unit 5: Knowledge Representation, Reasoning & Course Project
- 1. Introduction to PROLOG and program structure.
- 2. PROLOG program to categorize animals (and similar examples).
- 3. PROLOG solver for linear equation A*X + B = 0.
- 4. PROLOG program for family relationships (father, mother, uncle, grandson, etc.).
- 5. Build a simple chatbot in Python.
- 6. Perform sentiment classification with NLTK/sklearn.
- 7. Course Project Review-1: Problem Statement.
- 8. Course Project Review-2: Design Specification.
- 9. Course Project Review-3: Development & Results.
π Evaluation Schemes
| Component | Theory (CSE472) | Lab (CSP472) |
|---|---|---|
| Total Marks | 100 (CA: 25 + MSE: 15 + ESE: 60) | 100 (CA: 60 + ESE: 40) |
| Continuous Assessment |
β’ Assessment 1: 10 Marks (Units 1 & 2) β’ Assessment 2: 5 Marks (Units 3 & 4) β’ Assignment 1: 5 Marks (Units 1 & 2) β’ Assignment 2: 5 Marks (Units 3, 4, and 5) |
β’ Practical Records File β’ Performance in experiments (In-Class) β’ Course Project (Implementation, Working Source Code, and Results) β’ Viva-Voce (throughout semester) |
| Mid Semester Exam | 15 Marks | NA |
| End Semester Exam | 60 Marks | 40 Marks |
π Lectures
Theory lecture materials will be uploaded unit-wise. Each unit will include:
Unit 1: Introduction
Unit 2: Problem Solving
Unit 3: Knowledge Representation
Unit 4: Planning & Reasoning
Unit 5: Machine Learning
π§ͺ Artificial Intelligence Lab (CSP472)
π‘ Note: In the AI Lab, we will be using tools like CLIPS, PROLOG, Python, Google Colab, including various AI, ML, and DL libraries to perform the experiments.
π List of Experiments
Find the list of lab experiments with instructions and notes.
π View Experimentsπ Lab Manual
Download the official AI Lab Manual for complete guidelines.
β¬οΈ Download Lab Manualπ€ Lab Submissions
Please submit your lab reports and code files using the link below.
π Submit Your Workπ Assignments
π Common Instructions
- All assignments must be submitted before the due date.
- Upload your solution in PDF format.
- Plagiarism will not be tolerated.
- Late submissions may not be accepted.
- All assignments must be handwritten. Answers and solutions should be presented in a clear, step-by-step illustrative manner. Running text format will not be accepted.
- Students may be asked to explain their answers and solutions while obtaining the instructorβs signature. Grades will be awarded based on the explanation and understanding demonstrated.
- If any AI tools (such as GPTs) are used in preparing the assignment, students must also submit the complete script or prompt history along with the assignment.
π Assignment 1: Based on Unit-1 and 2
Due Date: 14th Feb 2026
- All questions are compulsory.
- Prepared and Submit as a single PDF file.
- Submit scanned handwritten PDF document using the link mentioned below.
π Submit Here
π Assignment 2: Planning and Machine Learning
Due Date: 30th September 2025
- Attempt any 5 out of 7 questions.
- Include clear reasoning steps.
- Submit scanned handwritten PDF document using the link mentioned below.
π Submit Here
β Post Your Doubts
Please fill in your details and doubt. Your submission will be recorded securely.
π Important Dates
- Assessment 1: 19th Feb to 23 Feb 2026 (Units 1 & 2) - Upcoming
- Assessment 2: 06 Apr to 12 Apr 2026 (Units 3 & 4)
- Assignment 1:
14 Feb 2026(Submission) βοΈ - Assignment 2: 20th March 2026 (Submission)
- Mid Semester Exam: 9th to 14th March 2026
- End Semester Exam: As per University Schedule
- Practical Records File: Ongoing (Weekly Check)
- Performance in Experiments: Continuous (In-Class)
- Course Project: Final Submission β 15 Apr 2026
- Viva-Voce: Throughout Semester
- End Semester Exam: As per University Schedule
π Course Feedback
π‘ Your feedback is extremely valuable in improving the course content and teaching effectiveness. Please take a few minutes to share your thoughts and suggestions with me.
π Fill Out the Feedback FormThanks From Your Course Instructor
Dear Students,
Thank you for your active participation in the course.
Your enthusiasm, curiosity, and commitment make this learning journey inspiring.
Keep asking questions, keep exploring, and never stop learning!
β Dr. Gopal Chandra Jana
(Course Instructor)