MACHINE LEARNING LAB

Paper Code: 
MCA 422C
Credits: 
02
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

The course enables the students to

  1. Understand the implementation procedures for machine learning algorithms.

2.     Design & develop Python/Java programs for various machine learning algorithms

3.     Apply algorithms on appropriate data sets.

4.     Compare performance of learning algorithms based on same techniques

  1. Identify and apply machine learning algorithms to solve real world problems.

 

Course Outcomes(COs):

 

Learning Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

 
 

CO250.        Implement procedures for the machine learning algorithms.

CO251.        Develop & design python programs for various machine learning algorithms

CO252.        Apply algorithms on appropriate data sets.

CO253.        Evaluate performances of different algorithms

CO254.        Categorize and apply machine learning algorithms to solve real world problems.

Approach in teaching:

Interactive Lab Sessions,

Modeling, Discussions, implementing enquiry based learning, student centered approach

 

Learning activities for the students:

Experiential Learning, Discussions, Lab Assignments, Learning through Real life data centric problems

·  Lab Assignments

·  Practical Record

·  Continues Assessment

·  Semester End Examination

 
 

 

Contents

 

1.  Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training samples. Read the training data from a .csv file.

 

2.  Implement working of the decision tree based ID3 algorithm using appropriate data set to classify it.

 

3.  Develop an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data set.

 

4.  Implement the naïve Bayesian classifier using appropriate data set and compute its accuracy, considering few data sets.

 

5.  Implement Bayesian network considering medical data. Use this model to demonstrate the diagnosis of Heart Disease Data Set.

 

6.  Implement EM algorithm to cluster a set of data stored in a .CSV file.

7.  Implement k-means algorithm to cluster same set of data as in experiment 6 and compare the results of these two algorithms and comment on the quality of clustering.

8.  Implement k-Nearest Neighbor algorithm to classify the iris data set and display both correct and incorrect predictions.

9.  Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Apply it on an appropriate data set and draw graph.

ESSENTIAL READINGS: 

  • Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer 2006
  • Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  • Joel Grus, “Data Science from Scratch- First Principles with Python”, O’Reilly, 2015

REFERENCES: 

  • Tom Mitchell, “ Machine Learning”, McGraw-Hill, 1997
  • Stephen MarsLand, “Machine Learning-An Algorithmic Perspective”, CRC Press, 2009
  • Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
  • M. Gopal, “Applied MACHINE LEARNING”, McGraw-Hill, 2018.
  • Mark Summerfield, “Programming in Python 3: A Complete Introduction to the Python Language”, Addison Wesley, 2010

Academic Year: 
2022-23
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