Machine Learning (Theory)

Paper Code: 
24DBCA501C
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

The objective of course is to:

  1. Introduce students to basic applications, concepts, and techniques of machine learning.
  2. Develop skills in students to implement machine learning algorithms on real world problems and evaluate their performance.

 

Course Outcomes: 

Course

Learning Outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course

 Code

Course

Title

24DBCA

501C

 

 

Machine

Learning

(Theory)

 

 

CO313. Formulate a problem for data analytics and analyses data and select suitable machine learning technique for designing a model.

CO314. Develop a machine learning model for unlabeled data using association rule.

CO315. Develop a machine learning model for unlabeled data using clustering technique.

CO316 Analyse and implement different classification techniques.

CO317. Analyse and implement different prediction techniques. Compare their performance.

CO318 Contribute effectively  in course-specific interaction.

Approach in teaching: Interactive Lectures, Discussion, Reading assignments, Demonstration.

 

Learning activities for the students: Self learning assignments, Effective questions, Seminar presentation.

Class test, Semester end examinations, Quiz, Assignments, Presentation.

 

 

9.00
Unit I: 

Introduction to Data Mining and machine learning:

Basic Data Mining Tasks, Data Mining versus Knowledge Discovery in Databases, Applications of Machine Learning, Machine Learning vs AI, Types of Machine Learning, Metrics, Accuracy Measures: Precision, recall, F-measure, confusion matrix, cross-validation.

 

9.00
Unit II: 

Understand the Problem by Understanding the Data:

unbalanced data, Unsupervised Learning: Association rules, Apriori algorithm, FP tree algorithm, and their implementation in Python, Market Basket Analysis and Association Analysis.

 

9.00
Unit III: 

Clustering:

k-means and implementation of k-means using python, Concept of other clustering algorithms: Expectation Maximization (M) algorithm, Hierarchical clustering, and DBSCAN.

 

9.00
Unit IV: 

Classification & Prediction:

model Construction, performance, attribute selection Issues: under, Over-fitting, cross validation, tree pruning methods, missing values, Information Gain, Gain Ratio, Gini Index, continuous classes. Classification and Regression Trees (CART) and C 5.0. Implementation of decision tree in python

 

9.00
Unit V: 

Classification & Prediction:

Linear Regression, Multiple Linear Regression, Logistic Regression, Naïve Bayes and Support Vector Machines(SVM), Implementation of Linear Regression, Logistic Regression, Naïve Bayes and SVM in python.

 

ESSENTIAL READINGS: 
  1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  2. Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Second Edition,Packt>.
  3. McKinney, Python for Data Analysis. O’ Reilly Publication,2017.

 

REFERENCES: 

SUGGESTED READINGS:

1.    Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018. (Latest editions of the above books are to be referred)

e RESOURCES:

1.    https://www.jigsawacademy.com/blogs/business-analytics/

2.    NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212

3.    Python, w3scool, https://www.w3schools.com/

4.    Jupiter :www.jupiter.com

5.    Googlecolab: www.googlecolab.com

JOURNALS:

1.    Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr

 International Journal of Machine Learning and Cybernetics, springer : https://www.springer.com/journal/13042 

Academic Year: 
2024-25
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