Department Events
MACHINE LEARNING
Course Objectives
This course enables the students to
- Understand the basic concepts, approaches and techniques in Machine Learning
- Comprehend concept of supervised and unsupervised learning & other advanced learning models.
- Understand modern notions in data analysis oriented computing
- Learn to evaluate machine learning models
- Relate and apply the learned algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
- Develop the basic skills necessary to pursue research in Machine Learning.
Course Outcomes(COs):
Learning Outcome (at course level)
| Learning and teaching strategies | Assessment Strategies |
| Approach in teaching: Interactive Lectures, Modeling, Discussions, implementing enquiry based learning, student centered approach, Research problem based discussions
Learning activities for the students: Experiential Learning, Presentations, Discussions, Quizzes and Assignments
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Introduction
Machine Learning, Machine Learning Foundations-Overview, Applications, Types of Machine Learning, Basic Concepts in Machine Learning – Examples of Machine Learning, Perspectives/Issues in Machine Learning, AI vs. Machine Learning, Introduction to Python.
Supervised Learning
Introduction, Linear Models of Classification – Decision Trees, Naïve Bayes Classification, Linear Regression – Logistic Regression – Bayesian Logistic Regression – Probabilistic Models Neural Network-Feed Forward Network Functions – Error Back Propagation – Regularization - Bayesian Neural Networks – Radial Basis Function Networks, Ensemble Methods – Random Forest – Bagging – Boosting.
Unsupervised Learning
Clustering, K-Means Clustering, EM (Expectation Maximization), Mixtures of Gaussians, EM algorithm in General, The Curse of Dimensionality, Dimensionality Reduction, Factor Analysis, Principal Component Analysis, Probabilistic PCA, Independent Component Analysis. Challenges for Big Data Analytics.
Probabilistic Graphical Models
Directed Graphical Models, Bayesian Networks, Exploiting Independence Properties, From Distributions to Graphs, Examples – Markov Random Fields – Inference In Graphical Models – Learning - Naïve Bayes Classifiers – Markov Models – Hidden Markov Models. Undirected graphical Models – Markov Random Fields – Conditional Independence Properties.
Advanced Learning
Sampling – Basic Sampling Method – Monte Carlo, Reinforcement Learning-Introduction-The Learning Task, Instance based Learning-Nearest neighbor classification, k-nearest neighbor, nearest neighbor error probability, Elements of Reinforcement Learning, Difference between Reinforcement Learning and Supervised Learning, Applications of Reinforcement Learning, Model based learning, Semi-Supervised Learning, Computational Learning Theory.
· 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
