Machine Learning –I

Paper Code: 
MBB 227
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course Objectives: The objective of course is to:

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

 

Course Outcomes (COs):

 

Course

Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Paper Code-

 

 

Paper Title

CLO 89.Formulate a problem for business analytics.

CLO 90. Install python and orange tool for machine learning implementation on business problem.

CLO 91.Prepare the dataset for computation after collected it from the business domain based data source.

CLO 92.Select suitable machine learning technique for designing a model.

CLO 93.Develop a machine learning model for business problems.

CLO 94. Evaluate and compare the performance of machine learning models.

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, Exercises related with Machine Learning algorithm, presentations

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

MBB 227

                                    

Machine Learning –I

 

 

18.00

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, bootstrap,   Probability and likelihood, probability distribution. Data Mining tool Orange.

 

18.00

Understand the Problem by Understanding the Data, unbalanced data, Unsupervised Learning : Association rules , Apriori algorithm, FP tree algorithm,  and their implementation in python and Orange tool, Market Basket Analysis and Association Analysis.

18.00

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

18.00

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 and Orange tool.

18.00

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 and Orange tool.

References: 

  • Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  • Sebastian Raschka & Vahid Mirjalili,” Python Machine Learning”, Second Edition,Packt>.
  • McKinney ,Python for Data Analysis. O’ Reilly Publication,2017.
  • Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas"

(Latest editions of the above books are to be referred)

Academic Year: 
2021-2022
  • Printer-friendly version
  • PDF version