Machine Learning CO PO PSO

 

MACHINE LEARNING

Syllabus

UNIT - I

Learning - Types of Machine Learning - Supervised Learning - The Brain and the Neuron - Design a Learning System - Perspectives and Issues in Machine Learning - Concept Learning Task - Concept Learning as Search - Finding a Maximally Specific Hypothesis - Version Spaces and the Candidate Elimination Algorithm - Linear Discriminants: - Perceptron - Linear Separability - Linear Regression.

UNIT - II

Multi-layer Perceptron- Going Forwards - Going Backwards: Back Propagation Error - Multi-layer Perceptron in Practice - Examples of using the MLP - Overview - Deriving Back-Propagation - Radial Basis Functions and Splines - Concepts - RBF Network - Curse of Dimensionality - Interpolations and Basis Functions – Support Vector Machines

UNIT - III

Learning with Trees - Decision Trees - Constructing Decision Trees - Classification and Regression Trees - Ensemble Learning - Boosting - Bagging - Different ways to Combine Classifiers - Basic Statistics - Gaussian Mixture Models - Nearest Neighbor Methods - Unsupervised Learning - K means Algorithms

UNIT - IV

Dimensionality Reduction - Linear Discriminant Analysis - Principal Component Analysis - Factor Analysis - Independent Component Analysis - Locally Linear Embedding - Isomap - Least Squares Optimization

Evolutionary Learning - Genetic algorithms - Genetic Offspring: - Genetic Operators - Using Genetic Algorithms

UNIT - V

Reinforcement Learning - Overview - Getting Lost Example

Markov Chain Monte Carlo Methods - Sampling - Proposal Distribution - Markov Chain Monte Carlo - Graphical Models - Bayesian Networks - Markov Random Fields - Hidden Markov Models - Tracking Methods

 

 TEXT BOOKS:

1.                  Stephen Marsland, Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.

ONLINE NOTES:

https://codingacharya.blogspot.com/2026/07/machine-learning-theory-ace.html

 

Lesson Plan/Teaching Plan

Lesson Plan / Teaching Plan — Machine Learning
Duration: 29-Jun-2026 to 28-Nov-2026  |  Assumed load: 4 hrs/week (adjust to your institution's timetable)
Week	Dates	Unit	Topics to be Covered	Hrs	CO(s)	Remarks / Holidays
1	29 Jun – 4 Jul 2026	I	Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron	4	CO1
2	6 – 11 Jul 2026	I	Design a Learning System – Perspectives and Issues in Machine Learning	4	CO1
3	13 – 18 Jul 2026	I	Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis	4	CO1
4	20 – 25 Jul 2026	I	Version Spaces and the Candidate Elimination Algorithm	4	CO1
5	27 Jul – 1 Aug 2026	I	Linear Discriminants – Perceptron, Linear Separability, Linear Regression (Unit I wrap-up)	4	CO1 / CO2
6	3 – 8 Aug 2026	I / II	Internal Assessment-I (Unit I)  |  Unit II begins: Multi-layer Perceptron – Going Forwards	4	CO2	IA-I week
7	10 – 15 Aug 2026	II	Going Backwards: Back-Propagation Error	3	CO2	15 Aug – Independence Day (holiday)
8	17 – 22 Aug 2026	II	Multi-layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation	4	CO2
9	24 – 29 Aug 2026	II	Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality	4	CO2
10	31 Aug – 5 Sep 2026	II	Interpolations and Basis Functions – Support Vector Machines (Unit II wrap-up)	4	CO2
11	7 – 12 Sep 2026	II / III	Revision Units I–II  |  Unit III begins: Learning with Trees – Decision Trees, Constructing Decision Trees	4	CO2
12	14 – 19 Sep 2026	III	Classification and Regression Trees – Ensemble Learning – Boosting – Bagging	4	CO4
13	21 – 26 Sep 2026	III	Different ways to Combine Classifiers – Basic Statistics	4	CO4
14	28 Sep – 3 Oct 2026	III	Gaussian Mixture Models – Nearest Neighbor Methods	4	CO2	2 Oct – Gandhi Jayanti (holiday)
15	5 – 10 Oct 2026	III	Unsupervised Learning – K-means Algorithm (Unit III wrap-up)  |  Internal Assessment-II (Units II–III)	4	CO1 / CO4	IA-II week
16	12 – 17 Oct 2026	IV	Dimensionality Reduction – Linear Discriminant Analysis, Principal Component Analysis	4	CO2	Dussehra period – verify local holiday calendar
17	19 – 24 Oct 2026	IV	Factor Analysis – Independent Component Analysis	4	CO2
18	26 – 31 Oct 2026	IV	Locally Linear Embedding – Isomap – Least Squares Optimization	4	CO2
19	2 – 7 Nov 2026	IV	Evolutionary Learning – Genetic Algorithms, Genetic Offspring, Genetic Operators, Using Genetic Algorithms (Unit IV wrap-up)	4	CO3	Diwali period – verify local holiday calendar
20	9 – 14 Nov 2026	V	Reinforcement Learning – Overview – Getting Lost Example	4	CO1
21	16 – 21 Nov 2026	V	Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo	4	CO1
22	23 – 28 Nov 2026	V	Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models – Tracking Methods (Unit V wrap-up)  |  Internal Assessment-III / Revision	4	CO1	IA-III / pre-final revision week
Note: Dates assume a Mon-start week and 4 teaching hours/week; adjust to your actual timetable. Holiday dates for Dussehra/Diwali 2026 are approximate — please verify against your institution's official academic calendar. CO mapping for Unit V (RL, MCMC, graphical models) is drafted loosely against CO1 since the given Course Outcomes list does not explicitly cover these topics; consider adding a CO5 if your program wants a dedicated outcome for this unit.

 

 

1. Course Details

Item	Details
Course Title	Machine Learning
Course Code
Category	Professional Core / Program Core
L-T-P-C	3-0-0-3 (suggested)
Credits	3
Contact Hours	45 Hours
Prerequisites	Probability and Statistics, Linear Algebra, Programming Fundamentals
Department	CSM
Course Type	Theory
Semester	v
Regulation

 

2. Course Objectives

The course aims to:

1. Explain the fundamental principles, learning paradigms, and mathematical foundations of machine learning.
2. Develop an understanding of supervised, unsupervised, and reinforcement learning techniques for solving computational problems.
3. Apply neural networks, decision trees, ensemble methods, support vector machines, and nearest-neighbor techniques to learning tasks.
4. Analyze dimensionality reduction, evolutionary learning, probabilistic models, and optimization techniques used in machine learning.
5. Develop the ability to select and employ appropriate machine learning methods for practical classification, regression, clustering, and sequential decision-making problems.

 

3. Course Outcomes

CO	Course Outcome	BTL
CO1	Explain the fundamental concepts of machine learning and interpret concept-learning approaches, linear discriminants, perceptrons, and regression models.	BTL 2 – Understand
CO2	Apply multilayer perceptrons, back-propagation, radial basis function networks, and support vector machines to suitable learning problems.	BTL 3 – Apply
CO3	Analyze decision trees, ensemble learning techniques, statistical models, nearest-neighbor methods, and clustering algorithms for different data-analysis tasks.	BTL 4 – Analyze
CO4	Examine dimensionality-reduction and optimization techniques, including PCA, LDA, ICA, LLE, Isomap, and least-squares methods, for high-dimensional datasets.	BTL 4 – Analyze
CO5	Design suitable evolutionary learning solutions using genetic algorithms and their operators for optimization and search problems.	BTL 6 – Create
CO6	Evaluate reinforcement-learning and probabilistic graphical-model approaches, including MCMC, Bayesian networks, Markov random fields, HMMs, and tracking methods, for sequential and uncertain environments.	BTL 5 – Evaluate

 

BTL Distribution

• CO1 → BTL 2
• CO2 → BTL 3
• CO3 → BTL 4
• CO4 → BTL 4
• CO5 → BTL 6
• CO6 → BTL 5

BTL 1 is not used for any CO.

 

4. Syllabus

UNIT I – Introduction to Machine Learning and Concept Learning

Learning: Types of Machine Learning, Supervised Learning, The Brain and the Neuron, Design of a Learning System, Perspectives and Issues in Machine Learning.

Concept Learning: Concept Learning Task, Concept Learning as Search, Finding a Maximally Specific Hypothesis, Version Spaces and Candidate Elimination Algorithm.

Linear Discriminants: Perceptron, Linear Separability, Linear Regression.

Suggested Hours: 9

 

UNIT II – Neural Networks, RBF and Support Vector Machines

Multi-layer Perceptron: Going Forwards, Going Backwards – Back Propagation Error, Multi-layer Perceptron in Practice, Examples of Using MLP.

Back Propagation: Overview, Deriving Back-Propagation.

Radial Basis Functions and Splines: Concepts, RBF Network, Curse of Dimensionality, Interpolations and Basis Functions.

Support Vector Machines.

Suggested Hours: 9

 

UNIT III – Decision Trees, Ensemble and Unsupervised Learning

Learning with Trees: Decision Trees, Constructing Decision Trees, Classification and Regression Trees.

Ensemble Learning: Boosting, Bagging, Different Ways to Combine Classifiers.

Basic Statistics: Gaussian Mixture Models.

Nearest Neighbor Methods.

Unsupervised Learning: K-Means Algorithms.

Suggested Hours: 9

 

UNIT IV – Dimensionality Reduction and Evolutionary Learning

Dimensionality Reduction: Linear Discriminant Analysis, Principal Component Analysis, Factor Analysis, Independent Component Analysis, Locally Linear Embedding, Isomap, Least Squares Optimization.

Evolutionary Learning: Genetic Algorithms, Genetic Offspring, Genetic Operators, Using Genetic Algorithms.

Suggested Hours: 9

 

UNIT V – Reinforcement Learning, MCMC and Graphical Models

Reinforcement Learning: Overview, Getting Lost Example.

Markov Chain Monte Carlo Methods: Sampling, Proposal Distribution, Markov Chain Monte Carlo.

Graphical Models: Bayesian Networks, Markov Random Fields, Hidden Markov Models, Tracking Methods.

Suggested Hours: 9

 

Total: 45 Hours

 

Unit	Topic	Hours
I	Introduction, Concept Learning, Linear Discriminants	9
II	MLP, Backpropagation, RBF, SVM	9
III	Trees, Ensemble Learning, GMM, KNN, K-Means	9
IV	Dimensionality Reduction, Evolutionary Learning	9
V	Reinforcement Learning, MCMC, Graphical Models	9
Total		45

 

5.      Program Outcomes – POs

PO	Program Outcome
PO1	Engineering Knowledge: Apply knowledge of mathematics, science, engineering fundamentals, and specialization to solve complex engineering problems.
PO2	Problem Analysis: Identify, formulate, review research literature, and analyze complex engineering problems using first principles of mathematics, natural sciences, and engineering sciences.
PO3	Design/Development of Solutions: Design creative solutions for complex engineering problems and design system components that meet specified needs with appropriate consideration for public health, safety, and cultural, societal, and environmental considerations.
PO4	Conduct Investigations of Complex Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of information to provide valid conclusions.
PO5	Modern Tool Usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools to complex engineering activities.
PO6	The Engineer and Society: Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues and the consequent responsibilities relevant to professional engineering practice.
PO7	Environment and Sustainability: Understand and assess the impact of professional engineering solutions in societal and environmental contexts and demonstrate knowledge of sustainable development.
PO8	Ethics: Apply ethical principles and commit to professional ethics, responsibilities, and norms of engineering practice.
PO9	Individual and Team Work: Function effectively as an individual and as a member or leader in diverse teams and multidisciplinary settings.
PO10	Communication: Communicate effectively on complex engineering activities with the engineering community and society through effective reports, documentation, presentations, and instructions.
PO11	Project Management and Finance: Demonstrate knowledge and understanding of engineering and management principles and apply these to one's own work as a member or leader of a team to manage projects in multidisciplinary environments.
PO12	Life-long Learning: Recognize the need for and possess the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

 

 

6.      Program Specific Outcomes – PSOs

PSO	Program Specific Outcome
PSO1	Develop computational solutions using programming, data structures, algorithms, artificial intelligence, machine learning, and data analytics techniques for real-world problems.
PSO2	Design and evaluate intelligent software systems using modern computational tools, data-driven methodologies, and emerging technologies to address multidisciplinary applications.

 

7.      CO–PO Mapping Matrix

Mapping Scale

• 3 – High correlation
• 2 – Moderate correlation
• 1 – Low correlation
• – – No significant correlation

 

CO / PO	PO1	PO2	PO3	PO4	PO5	PO6	PO7	PO8	PO9	PO10	PO11	PO12
CO1	3	2	1	1	2	–	–	–	–	–	–	2
CO2	3	3	3	2	3	–	–	–	1	1	–	2
CO3	3	3	2	3	3	1	–	–	1	1	–	2
CO4	3	3	2	3	3	–	1	–	–	1	–	2
CO5	3	3	3	2	3	–	1	–	2	1	2	2
CO6	3	3	2	3	3	1	1	1	1	1	–	3

 

CO–PSO Mapping

CO / PSO	PSO1	PSO2
CO1	3	2
CO2	3	3
CO3	3	3
CO4	3	3
CO5	3	3
CO6	3	3

 

 

CO–PO–PSO Consolidated Matrix

CO	PO1	PO2	PO3	PO4	PO5	PO6	PO7	PO8	PO9	PO10	PO11	PO12	PSO1	PSO2
CO1	3	2	1	1	2	–	–	–	–	–	–	2	3	2
CO2	3	3	3	2	3	–	–	–	1	1	–	2	3	3
CO3	3	3	2	3	3	1	–	–	1	1	–	2	3	3
CO4	3	3	2	3	3	–	1	–	–	1	–	2	3	3
CO5	3	3	3	2	3	–	1	–	2	1	2	2	3	3
CO6	3	3	2	3	3	1	1	1	1	1	–	3	3	3

 

Overall CO design

The COs deliberately progress from understanding → application → analysis → analysis → creation → evaluation, while excluding BTL 1 entirely. This also gives reasonable coverage of the five units without making every CO unnecessarily high-level.

Teachning and Learning Materials

PPT link:

https://docs.google.com/presentation/d/e/2PACX-1vQJ9LO4IgLmN801Y1oPKVJ8hmUdVhez9JK7xOMLEbKqFP1ohqaUS-BjJdEwj9x8yA/pub?start=false&loop=false&delayms=3000&slide=id.p1

Notes:

https://codingacharya.blogspot.com/2026/07/machine-learning-theory-ace.html

TEXT BOOKS:

1.                  Stephen Marsland, Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.

 

Practical Projects:

https://codingacharya.blogspot.com/2026/07/machine-learning-lab-practice.html

https://github.com/codingacharya?tab=repositories