Subject Name: Introduction to Machine Learning (IML)

Class: B.Tech. - III year (EEE)

Semester: VI SEM

Academic Year : 2024 - 2025

Resource Materials:           Unit - I                Unit - II               Unit - III                 Unit - IV                 Unit - V          

20AD83-INTRODUCTION TO MACHINE LEARNING

Pre-requisite: Probability and Statistics

Course Educational Objective: The objective of the course is to provide the basic concepts and techniques of Machine Learning and help to use recent machine learning approaches for solving practical problems. It enables students to gain experience to do independent study and research.

Course Outcomes: At the end of this course, the student will be able to

              CO1: Identify the characteristics of machine learning. (Understand- L2)

              CO2: Understand the Model building and evaluation approaches. (Understand- L2) 

             CO3: Apply regression algorithms for real-world Problems. (Apply- L3)

CO4: Handle classification problems via supervised learning algorithms. (Apply-L3) 

CO5: Learn advanced learning techniques to deal with complex data. (Apply- L3)

UNIT I

Introduction to Machine Learning - Introduction, Types of Machine Learning, Applications of Machine Learning, Issues in Machine Learning. Preparing to Model- Introduction, Machine Learning Activities, Basic Types of Data in Machine Learning, Exploring Structure of Data, Data Quality and Remediation, Data Pre-Processing.

UNIT II

Modeling & Evaluation- Introduction, selecting a Model, training a Model (for Supervised Learning), Model Representation and Interpretability, Evaluating Performance of a Model.

Basics of Feature Engineering- Introduction, Feature Transformation – Feature Construction, Feature Extraction, Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Linear Discriminate Analysis (LDA), Feature Subset Selection

UNIT III

Regression : Introduction to regression analysis, Simple linear regression, Multiple linear regression, Assumptions in Regression Analysis, Main Problems in Regression Analysis, Improving Accuracy of the linear regression model, Polynomial Regression Model, Logistic Regression, Regularization, Regularized Linear Regression, Regularized Logistic Regression.

                                               UNIT IV

Supervised Learning: Classification- Introduction, Example of Supervised Learning, Classification Model, and Classification Learning Steps.

Common Classification Algorithms - k-Nearest Neighbor (kNN), Support vector Machines (SVM), Random Forest model.

                                                                           UNIT V

Other Types of Learning : Ensemble Learning- Bagging, Boosting, Stacking and its impact on bias and variance, Ada Boost, Gradient Boosting Machines, XG Boost. Reinforcement Learning - Introduction, Q Learning

                                         TEXTBOOKS

1.   Subramanian Chandra mouli, Saikat Dutt, Amit Kumar Das, “Machine Learning”, Pearson Education India ,1st edition,2015.

    2.   Tom M. Mitchell, “Machine Learning’, MGH, 1997.

                                   REFERENCE BOOKS

1.   Shai Shalev-Shwartz, ShaiBen David, “Understanding Machine Learning: From Theory to Algorithms”, Cambridge.

2.   Peter Harington, “Machine Learning in Action” , Cengage, 1st edition, 2012.

3.   Peter Flach, “Machine Learning: The art and science of algorithms that make sense of data”, Cambridge university press,2012.

4.   Jason Brownlee, “Machine Learning Mastery with Python Understand Your Data, Create Accurate Models and Work Projects End-To-End”, Edition: v1.4, 2011.