Advanced Machine Learning (Theory)

Paper Code: 
24CBDA411
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

This course will enable students to 

  1. Learn the concepts of different machine learning techniques.
  2. Apply and build Models in the context of real world problems.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course

 Code

Course

Title

CO199. Analyse data using dimensionality reduction techniques on real world problem.

CO200. Identify and implement neural network techniques for solving complex problems.

CO201. Appraise the significance of deep learning in different problem domain.

CO202. Build ensemble methods, bagging and random forests for prescriptive analysis and recommendation systems.

CO203. Design and evaluate the performance of machine learning models for diverse domains.

CO204.Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Group Discussion, Case Study

 

Learning activities for the students:

Self-learning assignments, Machine Learning exercises, presentations

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

24CBDA411

Advanced Machine Learning

(Theory)

 

 

9.00
Unit I: 
Dimensionality Reduction:

Introduction to Dimensionality Reduction, Components of Dimensionality Reduction, Methods of Dimensionality Reduction, Principal component analysis, employing PCA using python Self-organizing maps, employing SOM using python

 

9.00
Unit II: 
Artificial Neural Network:

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

9.00
Unit III: 
Deep learning:

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

 

9.00
Unit IV: 
Ensemble methods and Recommendation:

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

 

9.00
Unit V: 
Case studies:

Bike Sharing trends, customer segmentation and effective cross selling, analysing wine types and quality, forecasting stock and commodity prices.

ESSENTIAL READINGS: 
SUGGESTED TEXT BOOKS
  1. Advanced Machine Learning with Python, Hearty John,Packt,2016.
  2. Brian Boucheron , Lisa Tagliaferri,Machine Learning projects, DigitalOcean

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS

  1. Madhavan, “Mastering Python for Data Science”, Packt, 2015.

e RESOURCES

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

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

3. Jupiter :www.jupiter.com

4. Googlecolab: www.googlecolab.com

JOURNALS

  1. Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr
  2.  International Journal of Machine Learning and Cybernetics, springer : https://www.springer.com/journal/13042 

 

 

Academic Year: 
2024-25
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