Department Events
MACHINE LEARNING LAB
Course Objectives:
The course enables the students to
- Understand the implementation procedures for machine learning algorithms.
- Design & develop Python/Java programs for various machine learning algorithms
- Apply algorithms on appropriate data sets.
- Compare performance of learning algorithms based on same techniques
- 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 |
| 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 |
|
Contents:
- 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.
- Implement working of the decision tree based ID3 algorithm using appropriate data set to classify it.
- Develop an Artificial Neural Network by implementing the Back propagation algorithm and test the same using appropriate data set.
- Implement the naïve Bayesian classifier using appropriate data set and compute its accuracy, considering few data sets.
- Implement Bayesian network considering medical data. Use this model to demonstrate the diagnosis of Heart Disease Data Set.
- Implement EM algorithm to cluster a set of data stored in a .CSV file.
- 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.
- Implement k-Nearest Neighbor algorithm to classify the iris data set and display both correct and incorrect predictions.
- Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Apply it on an appropriate data set and draw graph.
· 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
Suggested Readings:
· 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
E-Resources
- Introduction to Machine Learning, By NPTEL, https://nptel.ac.in/courses/106106139
- Machine Learning, By Coursera, https://www.coursera.org/learn/machine-learning
- Machine Learning for Everyone, By Datacamp, https://www.datacamp.com/courses/machine-learning-for-everyone?tap_a=5644-dce66f&tap_s=950491-315da1&utm_source=adwords_ppc&utm_medium=cpc&utm_campaignid=1455363063&utm_adgroupid=65083631908&utm_device=c&utm_keyword=&utm_matchtype=&utm_network=g&utm_adpostion=&utm_creative=278443377110&utm_targetid=dsa-498578056204&utm_loc_interest_ms=&utm_loc_physical_ms=9061781&gclid=Cj0KCQjwr-SSBhC9ARIsANhzu17mWLZBN2damrX7dfvMyE2_7HEKUJUzyUv5ADvNkex5rlHS6rldUa4aAjYDEALw_wcB
- Introduction to Machine Learning, By Udacity, https://www.udacity.com/course/intro-to-machine-learning--ud120
