ADVANCED MACHINE LEARNING

Paper Code: 
CBDA 411
Credits: 
3
Periods/week: 
3
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 (COs). 

Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

 

On completion of this course, the students will:

CO166. Recognize nonlinear problems in application domain and formulate them for analysis 

CO167. Compare machine learning algorithms and select a suitable algorithm to handle nonlinear problems.

CO168. Extract dataset and transform them for computation.

CO169. Design machine learning model to solve the problems and interpret their results

CO170. Analyse, synthesize and compare machine learning algorithms for different problems and evaluate the performance of machine learning models using different ML metrics.

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study

 

Learning activities for the students:

Self-learning assignments, Machine Learning exercises, presentations

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

 

 

9.00
Unit I: 
Principal component analysis, employing PCA using python Self-organizing maps, employing SOM using python
 
 
 
9.00
Unit II: 
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 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, 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, analyzing wine types and quality, forecasting stock and commodity prices.

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

 

 

REFERENCES: 
1. Madhavan, “Mastering Python for Data Science”, Packt, 2015.
2. McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
 
 
E RESOURCES
  • NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212
  • Python, w3scool, https://www.w3schools.com/
  • Jupiter :www.jupiter.com
  • Googlecolab: www.googlecolab.com
 
JOURNALS
 
  • 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: 
2023-24
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