Advanced Machine Learning Lab

Paper Code: 
25CBDA412
Credits: 
03
Periods/week: 
06
Max. Marks: 
100.00
Objective: 

This course will enable students to

1.   Learn  and  apply  different machine learning techniques in python environment in different scenarios.

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

CO205.   Identify linear            and nonlinear problems in an        application domain      and formulate them for analysis

CO206. Select  a suitable algorithm to handle nonlinear problems.

CO207.          Extract dataset,   pre-process and    transform  them for computation. CO208.          Design machine          learning model    to   solve    the problems                 and interpret their  results. CO209. Evaluate  the performance            of machine          learning models     using       ML metrics. CO210.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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

25CBDA412

 

 

 

 

 

 

 

 

 

 

 

 

 

Advanced Machine Learning Lab (Practical)

 

Exercises based on the following topics:

  1. Importing libraries  tensorflow, keras, sklearn
  2. Implementing MLP,KNN and  RBM
  3. Implementing deep learning algorithms
  4. Implementing ensemble algorithms
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. 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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