Course Hand-out
Program : B.Tech
Academic session : Spring Semester- 2023 (Even Semester)
Subject Code : CS-3035
Subject name : Machine Learning (ML)
Semester : 6th Semester
L-T-P Structure : 3-0-0
Course Faculty : Dr. Babita Panda
Course Objectives
To introduce students to the basic concepts and techniques of Machine Learning.
To understand a range of machine learning algorithms along with their strengths and weaknesses.
To develop skills of using recent machine learning software for solving real-world problems.
To gain experience of doing independent study and research.
Course Outcomes
CO 1: Ability to have a good understanding of the fundamental issues and challenges of machine learning.
CO 2: Ability to develop an appreciation for what is involved in learning from data.
CO 3: Ability to have an understanding of the strengths and weaknesses of many popular machine learning
approaches
CO 4: Ability to appreciate the underlying mathematical relationships within and across Machine Learning
algorithms and the paradigms of supervised and UN-supervised learning.
CO 5: Ability to apply the concept of regression methods, classificationmethods and clustering methods.
CO 6: Ability to design and implement various machine learning algorithms in a range of real-world applications
Lesson Plan
Date Class Module Name
Week TOPIC TO BE COVERED(but not limited to)
Number
12/01/23 1 INTRODUCTION TO Basic Understanding of Machine Learning
MACHINE LEARNING
2 Formulating a Machine Learning Problem and
1 13/01/23
Models: Special Emphasis on Target Function
3 Type of Machine Learning Problem:
16/01/23
Supervised, Unsupervised and Reinforced
2 4 FUNDAMENTALS Least Square Method
18/01/23
OF LEARNING
5 Nearest Neighbor Method
19/01/23
6 Distance Based Learning
20/01/23
7 LINEAR MODEL- Formulation & Mathematical Foundation of
23/01/23
LINEAR Regression Problem
3 27/01/23 8 REGRESSION The Regression Model & The Concepts of Least Squares
30/01/23 9 Error Reduction-Gradient Descent
ACTIVITY-I
02/02/23 10 GENERALISATION Over-fitting, Bias and Variance Relationship
4 03/02/23 11 LASSO Regression
06/02/23 12 RIDGE Regression
09/02/23 13 Nearest Neighbor Learning
5 10/02/23 14 CLASSIFICATION KNN Classification
13/02/23 15 Numerical Discussion
16 CLUSTERING Introduction to Unsupervised Learning, Distance
16/02/23
Metrics used
6
17/02/23 17 K-Means Approach for Clustering
20/02/23 18 Performance Evaluation and Stopping Criteria for K
Means
ACTIVITY-II
23/02/23 19 Limitation of Linear Model and Max Likelihood Learning
7 20 Link function and its Role in Handling Non Normal
GENERALISEDLINEAR
24/02/23 MODEL Kind of Data Distribution
27/02/23 21 Logistic Regression Introduction
02/03/23 22 LOGISTICREGRESSION
Likelihood Vs Probability
8
03/03/23 23 Logistic Regression Implementation
06/03/23 24 Logistic Regression Numerical
MID-SEMESTEREXAMINATION
20/03/23 25 TREE Idea of a tree based learner
BASED
9 23/03/23 26 LEARNER Steps in Decision Tree and Construction
24/03/23 27 Parameters of Decision Tree Performance
27/03/23 28 Numerical on Decision Tree
10 31/03/23 29 Stopping Criteria in Tree and Over-fitting Avoidance
03/04/23 30 Random Forest
ACTIVITY-III
06/04/23 31 PCA Principal Component Analysis
11 10/04/23 32 SUPPORT The idea of support vectors and its importance
VECTOR
13/04/23 33 MACHINE Derivation of Support Vector Equation
17/04/23 34 KKT Condition
12 20/04/23 35 Kernel Function: Dealing with nonlinearity
21/04/23 36 Polynomial and Radial Basis Kernel
ACTIVITY-IV
24/04/23 37 McCullough-Pitts Neuron Model
27/04/23 38 Perceptron Learning
13 28/04/23 39 NEURALNETWORK Back-propagation
01/05/23 40 Multi Layer Perceptron
04/05/23 41 Non-linear Problem Solving
05/05/23 42 A brief introduction to Deep Learning architecture
ACTIVITY-V
END SEMESTER EXAM
NOTE: Total number of classes is 42 which include lectures and tutorials etc.
Text Book:
1. Applied Machine Learning, M. Gopal, McGraw Hill Education
Reference Books:
1. Machine Learning March 1997, Thomas M. Mitchell, McGraw-Hill, Inc.
2. Neural Networks: A Comprehensive Foundation, Simon Haykin, Prentice
Hall
3. Neural Network Design, M. T. Hagan, H. B. Demuth, Mark Beale, Thomson
Learning,
Internal Evaluation (50 Marks):
Activities [Continuous evaluation] (30 Marks)
o Quiz(es)
o Assignment(s)
o Case Studies/Survey
o Presentation(s) etc.
Mid Semester Exam (20 Marks)
End Sem Exam (50 Marks):
