23AM01-MACHINE LEARNING
“Failure will never overtake me if my determination to succeed is strong enough.”
Saturday, 27 December 2025
Wednesday, 17 December 2025
23AM51-MACHINE LEARNING LAB
• To learn about computing central tendency measures and Data preprocessing techniques
• To learn about classification and regression algorithms
• To apply different clustering algorithms for a problem.
CO1: Apply
the appropriate pre-processing techniques on dataset.(Apply–L3)
CO2: Implement
supervised Machine Learning algorithms.(Apply–L3)
CO3: Implement
advanced Machine Learning algorithms.(Apply–L3)
CO4: Improve
individual/teamwork skills, communication & report writing skills with ethical
values.
TEXTBOOKS:
T1“MachineLearningTheoryandPractice”,MNMurthy,VS Ananthanarayana,UniversitiesPress(India),
2024.
REFERENCEBOOKS:
R1“Machine Learning”,Tom M.Mitchell,McGraw-HillPublication,2017
R2“MachineLearning inAction”,PeterHarrington,DreamTech.
R3“IntroductiontoDataMining”,
Pang-NingTan,MichelStenbach, VipinKumar,7thEdition,2019.
Software Required: Python/R/Weka
Lab should cover the concepts studied in the course work, sample list of Experiments:
1. Compute Central Tendency Measures: Mean, Median, Mode Measure of Dispersion:
Variance, Standard Deviation Cycle-1 Program.
2. Apply the following Pre-processing techniques for a given dataset. a. Attribute selection b. Handling Missing Values c. Discretization d. Elimination of Outliers.Cycle-2 Program1.py, Cycle -2 Program .ipynb file, Iris Dataset, Kidney-stone-Dataset.csv, Iris-Flower Dataset, Train_loan-Dataset, Cycle-2.1_ipynb, Cycle-2.2_ipynb, Cycle-2-Word, Cycle-2.1_word
3. Apply KNN algorithm for classification and regression.
Cycle-3 Program link , Cycle-3 Program .py file
4. Demonstrate decision tree algorithm for a classification problem and perform parameter tuning for
better results Cycle -4 & 5 Programs Link, Play-Tennis, Housing Data, Heart Disease Prediction, Iris, Program link2
5. Demonstrate decision tree algorithm for a regression problem
6. Apply Random Forest algorithm for classification and regression
7. Demonstrate Naïve Bayes Classification algorithm.
8. Apply Support Vector algorithm for classification
9. Demonstrate simple linear regression algorithm for a regression problem
10. Apply Logistic regression algorithm for a classification problem
11. Demonstrate Multi-layer Perceptron algorithm for a classification problem
12.Implement the K-means algorithm and apply it to the data you selected. Evaluate
performance by measuring the sum of the Euclidean distance of each example from
its class center. Test the performance of the algorithm as a function of the parameters K.
13. Demonstrate the use of Fuzzy C-Means Clustering
14. Demonstrate the use of Expectation Maximization based clustering algorithm
Wednesday, 3 September 2025
23CS51-COMPUTER PROGRAMMING LAB
23CS51-COMPUTER PROGRAMMING LAB
UNIT I
WEEK 1 Click the link
WEEK 2 Click the link for week 2 programs
WEEK 3 Click the link for Week 3 Programs Link 2
UNIT II
WEEK 4 Click the link for Week 4 Programs
WEEK 5 Click the link for Week 5 Programs
WEEK 7: Click the link for Week 7 Programs
Tuesday, 2 September 2025
INTRODUCTION TO PROGRAMMING_2025 -26
23CS01-INTRODUCTION TO PROGRAMMING
Pre-requisite : Mathematics, Basic Computer concepts
Course Objectives:
• To introduce students to the fundamentals of computer programming.
• To provide hands-on experience with coding and debugging.
• To foster logical thinking and problem-solving skills using programming.
• To familiarize students with programming concepts such as data types, control structures, functions, and arrays.
• To encourage collaborative learning and teamwork in coding projects.
Course Outcomes:
At the end of this course, the student will be able to
CO1: Understand basics of computers, concept of algorithms and flowcharts. (Understand-L2)
CO2: Understand the features of C language. (Understand-L2)
CO3: Interpret the problem and develop an algorithm to solve it. (Apply-L3)
CO4: Implement various algorithms using the C programming language. (Apply-L3)
CO5: Develop skills required for problem-solving and optimizing the code (Apply-L3)
UNIT – I Click the link for Unit 1 Material Link 2
Introduction to Programming and Problem Solving History of Computers, Basic organization of a computer: ALU, input-output units, memory, program counter, Introduction to Programming Languages, Basics of a Computer Program, Algorithms, flowcharts (Using Dia Tool), pseudo code. Introduction to Compilation and Execution, Primitive Data Types, Variables, and Constants, Basic Input and Output, Operations, Type Conversion, and Casting. Problem solving techniques: Algorithmic approach, characteristics of algorithm, Problem solving strategies: Top-down approach, Bottom-up approach, Time and space complexities of algorithms.
UNIT – II Click the link for unit-2 Material Extra Progs for Unit-2
Control Structures Simple sequential programs Conditional Statements (if, if-else, switch), Loops (for, while, do while) Break and Continue.
UNIT – III
Arrays and Strings Arrays indexing, memory model, programs with array of integers, two dimensional arrays, Introduction to Strings.
UNIT – IV
Pointers & User Defined Data types Pointers, dereferencing and address operators, pointer and address arithmetic, array manipulation using pointers, User-defined data types-Structures and Unions.
UNIT – V
Functions & File Handling Introduction to Functions, Function Declaration and Definition, Function call Return Types and Arguments, modifying parameters inside functions using pointers, arrays as parameters. Scope and Lifetime of Variables, Basics of File Handling
Textbooks:
1. "The C Programming Language", Brian W. Kernighan and Dennis M. Ritchie, Prentice Hall, 1988
2. Schaum’s Outline of Programming with C, Byron S Gottfried, McGraw-Hill Education, 1996
Reference Books:
1. Computing fundamentals and C Programming, Balaguruswamy, E., McGraw-Hill Education, 2008.
2. Programming in C, Rema Theeraja, Oxford, 2016, 2nd edition
3. C Programming, A Problem Solving Approach, Forouzan, Gilberg, Prasad, CENGAGE, 3rd edition