Regulation: R-2025
Semester: I (First Semester)
Branch: B.E. Computer Science and Engineering (Artificial Intelligence and Machine Learning)
Credits / L-T-P: 3 Credits | L-T-P: 2-0-2
Course Objectives
1. To introduce the basic components and operations of computers.
2. To develop problem-solving and computational thinking skills.
3. To enable learners to design simple solutions using algorithms and flowcharts.
4. To provide hands-on experience in visual programming and basic app development.
Full Unit-wise Syllabus
Unit I – Computers
Computer, Characteristics of Computers, History of Computers, Classification of Computers, Applications of Computers, Basic Organization of a Computer. Data Representation, Using spread sheets for basic operations on data and visualize the data.
Practical:
1. Office Software for documentation and presentation
2. Spread sheets for calculations and data. Visualization
Unit II – Computational Thinking
What is Computational Thinking, Decomposition, Abstraction, Real World Information to Computable Data, Number Systems, Conversions among Number systems, what is Logic, Boolean Logic, Applications of Propositional Logic.
Activities:
1. Solving problems based on number systems and logics.
2. Virtual Demonstration of Computational thinking
Unit III – Problem Solving Basics
Problem Definition, Logical Reasoning, Decomposition, Software Design Concept of an Algorithm, Algorithm Representation – Algorithm Discovery – Iterative Structures – Recursive Structures – Efficiency and Correctness - Implementation of Algorithms - Fundamental Algorithms: Exchanging the values of two variables, Counting, Summation of a set of numbers, Factorial computation, Generation of Fibonacci Sequence, Reversing the digits of an Integer, Base Conversion.
Activities: Algorithm Development for simple mathematical problems
Unit IV – Programming Languages
Program Development Life Cycle, Program Design Tools, Algorithms, Flowcharts, Pseudocodes, Role of Algorithms, Programming Languages, Programming Paradigms Traditional Programming Concepts, Procedural Units, Language Implementation, Declarative Programming.
Activities: Flowchart design for simple mathematical problems
Unit V – Scratch Programming
What is Scratch, Scratch Programming Environment, Paint Editor, Scratch Blocks, Arithmetic Operators and Functions, Use Motion Commands, Pen Commands and Easy Draw, Looks Palette, Sound Palette, Power of Repeat, Data Types, Variables, Getting Input from Users.
Making Decisions, Comparison Operators, Decision Structures, Logical Operators, Repetition, Loop Blocks, Stop Commands, Counters, Nested Loops, Recursion, String Processing, String Manipulation, Lists, Dynamic Lists, Numerical Lists, Searching and Sorting Lists.
Activities:
1. Creation of Functional Block for simple mathematical problems
2. Drawing and Painting operations
3. Scratch Animation for understanding Conditional and Loop statements.
4. Draw artistic, geometric patterns and create games.
5. Scratch Programs for applied scientific computing and data manipulations
Unit VI – App Development
Building Apps using problem, solving techniques on any app development platform, Modeling, incremental and iterative, reuse, modularization, algorithmic thinking, abstracting and modularizing, decomposition, testing and debugging.
Activities: Sample App Developments for societal problems.
Course Outcomes (COs)
CO1: Describe the basic components and functioning of computers, number systems, and data representation.
CO2: Apply computational thinking and problem-solving techniques to design simple algorithms for real-world problems.
CO3: Design and represent solutions using flowcharts, pseudocode, and basic visual programming tools.
CO4: Demonstrate the ability to independently learn new computing tools and practices essential for life-long learning.
Assessment Pattern (Quick Note)
- Weightage: Continuous Assessment 40% | End Semester Examination 60%
- Internal methodology: Assignments (10%), Quiz (5%), Project based learning (20%), Flipped Classroom (5%), Review of GATE questions (10%) & Internal Assessment: 50%
Source: Official Anna University B.E. CSE (AI & ML) R-2025 Syllabus
Last Updated: September 2026
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