ADVANCED MACHINE LEARNING LAB

Paper Code: 
CBDA 412
Credits: 
3
Periods/week: 
6
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 (COs). 

Course outcome (at course level)

Learning and teaching strategies

Assessment Strategies 

 

On completion of this course, the students will:

 

CO171. Identify linear and nonlinear problems in an application domain and formulate them for analysis 

CO172. Describe different machine learning algorithms and select a suitable algorithm to handle nonlinear problems.

CO173. Extract dataset, pre-process and transform them for computation.

CO174.  Design machine learning model to solve the problems and interpret their results

CO175. Evaluate the performance of machine learning models using ML metrics like RMSE ,accuracy etc.

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

 

 

Unit I: 

Exercises based on the following topics:

  • Importing libraries tensorflow,keras,sklearn
  • Implementing MLP,KNN and RBM 
  • Implementing deep learning algorithms
  • Implementing ensemble algorithms

 

 

REFERENCES: 

E RESOURCES

 

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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