This Artificial Intelligence (Theory & Lab) (CSE472 / CSP472) course content is designed for undergraduate students in the Department of Computer Science and Engineering at Sharda University. The course provides a comprehensive foundation in Artificial Intelligence concepts, problem-solving techniques, and real-world applications. It is structured into five major units: Unit 1: Introduction to Artificial Intelligence, which covers the foundations, goals, and intelligent agents; Unit 2: Problem Solving Agents, focusing on search techniques such as Uninformed Search Strategies (such as BFS; DFS; UCS; DLS; IDS), Informed Search Strategies (Greedy Best-First; A* Search; Heuristic Functions) and Minimax & Alpha-Beta Pruning; Unit 3: Knowledge and Reasoning, which introduces logic, inference mechanisms, and reasoning methods; Unit 4: Learning Paradigms and Artificial Neural Networks, covering supervised, unsupervised, and reinforcement learning along with ANN models; and Unit 5: Applications of Artificial Intelligence, highlighting real-world use cases and future trends in AI. The lab component (CSP472) complements theoretical concepts through hands-on implementation using Python, LISP, PROLOG, and opensource AI/ML libraries, enabling students to develop and analyze intelligent systems. Students gain practical experience in algorithm design, model development, and solving real-world AI problems through course projects. The course is supported by structured lecture notes, reference books, Additional PPTs and study material links, assignments, lab experiments, interactive tools, and evaluation schemes, ensuring a balanced approach to both conceptual understanding and practical skill development.
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 PARADIGMS and ANN
- 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 have been uploaded unit-wise. Please go through and review all the lecture content and additional materials carefully. If you have any doubts, post them using the βPost Doubtβ option.
Unit 1: Introduction
- π Lecture-1 PPT on Introduction to Artificial Intelligence (AI)
- π Lecture-2 PPT on Introduction to Intelligent Agent
- π Reference Book-1: Chapter-1 and 2, Page No. 1-59, "Artificial Intelligence: A Modern Approach", Third Edition, By Stuart Russell and Peter Norvig.
- π Additional Reference: Lecture PPT by Dr. Navid Asadi and Shayan (Sean) Taheri, University of Florida
Unit 2: Problem Solving Agent
- π Lecture-1 PPT on Problem Solving Agent: Solving Problems By Searching
- π Lecture-2 PPT on Minimax and Alpha-Beta Pruning
- π A* Algorithm Visualizer (Interactive) Try It is understand A-Star Algo
- π Minimax & Alpha-Beta Pruning Visualizer (Interactive) Try It is understand Minimax and Alpha-Beta Pruning concept
- π Reference Book-1: Chapter-3, Page No. 64-108, "Artificial Intelligence: A Modern Approach", Third Edition, By Stuart Russell and Peter Norvig.
- π Additional Reference
Unit 3: Knowledge and Reasoning
- π Lecture-1 PPT on Knowledge and Reasoning
- π Reference Book-1: Chapter-7,8 and 9, Page No. 234 - 357, "Artificial Intelligence: A Modern Approach", Third Edition, By Stuart Russell and Peter Norvig.
- π Additional Reference Book: "Knowledge Representation & Reasoning" by Brachman & Levesque.
- π Additional Reference Book PPT: "Knowledge Representation & Reasoning" by Brachman & Levesque.
Unit 4: Learning Paradigms and ANN
- π Lecture-1 PPT on Learning Paradigms
- π Lecture-2 PPT on Introduction to Artificial Neural Network (ANN) - SingleLayer and MultiLayer
- π Additional Reference PPT on "Machine Learning Paradigms" by Dr.Tom Rainforth, University of Oxford
- π Additional Reference PPT on "Neural Networks" by Dr.Tom Rainforth, University of Oxford
- π Additional Reference Courses by Dr.Tom Rainforth, University of Oxford For Understanding Mathematical Concept (very much required)
Unit 5: Applications of AI - Present & Future
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 your respective group link.
π¨βπ» Submission Link for Group 1 π©βπ» Submission Link for Group 2π 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: Based on Unit-3 and 4
Due Date: 27th March 2026
- All questions are compulsory.
- 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) βοΈ - Assessment 2:
06 Apr to 12 Apr 2026(Units 3 & 4) βοΈ - Assignment 1:
14 Feb 2026(Submission) βοΈ - Assignment 2:
20th March 2026(Submission) βοΈ - Mid Semester Exam:
9th to 14th March 2026βοΈ - End Semester Exam: As per University Schedule
- Practical Records File:
Ongoing (Weekly Check)βοΈ - Performance in Experiments:
Continuous (In-Class)βοΈ - Course Project:
Final Submission β 15 Apr 2026βοΈ - Viva-Voce:
Throughout SemesterβοΈ - End Semester Lab Exam (External):
As per University ScheduleβοΈ
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)