Fundamentals of Computing and Artificial Intelligence (CSAI1101)

This course on Fundamentals of Computing and Artificial Intelligence (CSAI1101) introduces the core concepts of Computer Science and Engineering, including computer organization, data representation, number systems and binary codes, and the evolution of programming languages. It develops problem-solving and computational thinking skills using visual tools such as mind maps, flowcharts, diagrams, and pseudocode, and emphasizes algorithm design, logical reasoning, decision tables, and step-by-step execution tracing. The course further introduces the fundamental principles of Artificial Intelligence (AI), intelligent agents, and rational behaviour, and covers the basics of Machine Learning (ML) including learning paradigms, classification, and clustering. It is a core theory course (2 credits) designed for students at Sharda University beginning their journey in Artificial Intelligence and Machine Learning.

📘 Syllabus (CSAI1101 - Theory)

Unit 1: Hardware Aspect of Computer Science & Engineering
- A. History of Computing Systems, Computer Basics and Computer Organization.
- B. Data Representation in Computers: Number System with Conversion, Binary Codes.
- C. Evolution of Programming Languages: Low Level Languages, High Level Languages, Machine Language, Assembly Language.
Unit 2: Visual Thinking and Problem Understanding
- A. Introduction to problem visualization: mental models vs external representations, benefits for computational thinking.
- B. Types of visual tools: sketches, mind maps, lists, tables, timelines, storyboards.
- C. Introduction to Algorithms & Pseudo code: Problem Solving Approach.
Unit 3: Visualizing Processes and Logic
- A. Flowchart fundamentals: symbols, control structures, branching, loops, input-output.
- B. Decision tables and truth tables for logical conditions.
- C. Step-by-step execution tracing: dry runs, state tables, variable tracking, test cases.
Unit 4: Introduction to Artificial Intelligence
- A. Fundamental concepts and principles of AI, Objectives and goals of intelligent systems, Historical evolution and milestones in AI development.
- B. Overview of the AI curriculum and core domains, Learning pathway for AI development, Real-world applications of AI across diverse domains.
- C. AI-based vs traditional problem-solving techniques, Rational agents and intelligent behaviour, Philosophical and practical perspectives of AI systems.
Unit 5: Introduction to Machine Learning
- A. Role and significance of machine learning within AI, Types of learning paradigms (supervised, unsupervised, reinforcement learning).
- B. Core concepts of classification and clustering, Basic algorithms and their working principles (Supervised Learning Algorithms).
- C. Applications of ML in various domains.

📊 Evaluation Schemes

Component	Theory (CSAI1101)
Total Marks	100 (CA: 25 + MSE: 15 + ESE: 60)
Continuous Assessment (CA)	• Assessment 1: 10 Marks (Units 1 & 2) • Assessment 2: 5 Marks (Units 3 & 4) • Assignment 1: 5 Marks (Units 1 & 2) • Assignment 2: 5 Marks (Units 3, 4 & 5)
Mid Semester Exam (MSE / MTE)	15 Marks
End Semester Exam (ESE / ETE)	60 Marks
Credits & Contact Hours	2 Credits \| L-T-P-SL: 2-0-0-2 \| 30 hrs Classroom + 30 hrs Self Learning = 60 hrs

📚 Lectures

Theory lecture materials will be uploaded unit-wise. Each unit will include:

📖 Textbooks & References

Textbooks

Peter Norton, Introduction to Computers, Tata McGraw Hill, Edition 6.
Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall.

References

E. Rich & K. Knight, Artificial Intelligence, Tata McGraw Hill, Edition 3.
Dan W. Patterson, Artificial Intelligence & Expert Systems, Pearson Education with Prentice Hall India, Indian Edition.
Virtual Labs — https://ps-iiith.vlabs.ac.in/

📑 Assignments

📌 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 instructor’s 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.

📘 Assignment 1: Based on Unit-1 and 2

Due Date: To be announced

All questions are compulsory.
Prepare and submit as a single PDF file.
Submit scanned handwritten PDF document using the link mentioned below.

📄 View Assignment

🔗 Submit Here

📘 Assignment 2: Based on Unit-3, 4 and 5

Due Date: To be announced

All questions are compulsory.
Include clear reasoning steps.
Submit scanned handwritten PDF document using the link mentioned below.

📄 View Assignment

🔗 Submit Here

❓ Post Your Doubts

Please fill in your details and doubt. Your submission will be recorded securely.

📅 Important Dates

Theory (CSAI1101)

Assessment 1: To be announced (Units 1 & 2)
Assessment 2: To be announced (Units 3 & 4)
Assignment 1: To be announced (Units 1 & 2)
Assignment 2: To be announced (Units 3, 4 & 5)
Mid Semester Exam: As per University Schedule
End Semester Exam: As per University Schedule

📝 Course Feedback

💡 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.

🚀 Fill Out the Feedback Form

Thanks 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!

— Dr. Gopal Chandra Jana
(Course Instructor)