Machine Learning -II

Paper Code: 
MBB 422
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course outcome (at course level)

Learning and

teaching Strategies

Assessment

Strategies

On completion of this course, the students will be able to;

CO 1.Recognize nonlinear problems in

business domain and formulate them for analysis

CO 2.Compare machine learning algorithms and select a suitable algorithm to handle nonlinear business problems.

CO 3.Extract dataset and transform them for computation.

CO 4.Design machine learning model to solve the problems and interpret their results

CO 5. Analyse, synthesize and compare machine learning algorithms for business problems.

CO 6.Evaluate the performance of machine learning models using ML metric like RMSE, accuracy etc.

Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activitiesfor the students: Self- learning assignments, presentations

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

 

18.00
Unit I: 

Principal component analysis, employing PCA using python Self-organizing maps, employing SOM using python

 

18.00
Unit II: 

Concept of Artificial Neural Networks, Types of neural networks, MLP, KNN, Restricted Boltzmann Machine, toplogy, training and applications of RBM. Implementation of MLP, KNN and RBM using python

 

 

18.00
Unit III: 

Deep belief networks, deep learning, applying and validating DBN, implementing deep learning using python, Autoencoders, denoising and applying autoencoders and assessing performance

 

18.00
Unit IV: 

Ensemble methods, bagging algorithms and random forest, employing random forest using python. Introduction to prescriptive analysis and recommendation system.

 

18.00
Unit V: 

Case studies: Bike Sharing trends, customer segmentation and effective cross selling, analyzing wine types and quality, forecasting stock and commodity prices.

*Case studies related to entire topics are to be taught.

Essential Readings: 
Essential readings
  • Advanced Machine Learning with Python, Hearty John,Packt (2016)
  • Brian Boucheron , Lisa Tagliaferri, Machine Learning projects, DigitalOcean
Suggested readings
  • McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
  • Curtis Miller,” Hands-On Data Analysis with NumPy and Pandas",Packt, 2015

 

References: 
E resources

 

Journals

 

Academic Year: 
2023-2024
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