Advanced Machine Learning

Paper Code: 
25CBDA411
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

 

 

 

 

 

 

 

 

 

 

 

 

 

25CBDA411

 

 

 

 

 

 

 

 

 

 

 

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: 

1.    Advanced Machine  Learning with Python, Hearty  John,Packt,2016.

2.  Brian Boucheron , Lisa Tagliaferri,Machine Learning projects, DigitalOcean

 

REFERENCES: 

SUGGESTED READINGS:

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

2.   McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.

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: 
2025-26
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