Coming soon
MACHINE LEARNING -II (Practical)
The course will enable students to comprehensively understand and apply advanced machine learning techniques including Principal Component Analysis, Self-Organizing Maps, Artificial Neural Networks, Deep Belief Networks, Ensemble Methods, and Prescriptive Analysis, with hands-on implementation using Python and reinforced by relevant case studies across various domains.
Course outcomes (Cos)
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Course |
Learning outcome (at course level) |
Learning and teaching strategies |
Assessment Strategies |
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Course Code |
Course Title |
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25MBB422 |
Machine Learning -II (Practical)
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CO655: Recognize nonlinear problems in business domain and formulate them for analysis CO656: Compare machine learning algorithms and select a suitable algorithm to handle nonlinear business problems. CO657: Extract dataset and transform them for computation. CO658: Design machine learning model to solve the problems and interpret their results CO659: Analyse, synthesize and compare machine learning algorithms for business problems. CO660: Contribute effectively in course-specific interaction |
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 |
Principal component analysis, employing PCA using python Self-organizing maps, employing SOM using python
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
Deep belief networks, deep learning, applying and validating DBN, implementing deep learning using python, Autoencoders, denoising and applying autoencoders and assessing performance
Ensemble methods, bagging algorithms and random forest, employing random forest using python. Introduction to prescriptive analysis and recommendation system.
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.
• 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
E resources
• Inflibnet Shodhganga, www.shodhganga.inflibnet.ac.in
• NPTEL Local Guru content
• https://nptel.ac.in/courses/106106139
• https://www.w3schools.com/
Journals
• https://vciba.springeropen.com/
• https://epjdatascience.springeropen.com/
