MACHINE LEARNING

Paper Code: 
24MCA421C
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

This course enables the students to

  1. Understand the basic concepts, approaches and techniques in Machine Learning
  2. Comprehend concept of supervised and unsupervised learning & other advanced learning models.
  3. Understand modern notions in data analysis oriented computing
  4. Learn to evaluate machine learning models
  5. 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.
  6. Develop the basic skills necessary to pursue research in Machine Learning.

 

Course Outcomes: 

Course

Learning Outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

24MCA 421C

Machine Learning

(Theory)

 

  1. Examine the foundations and working concept of machine learning
  2. Compare different machine learning models & study different probabilistic graph based models and their applications
  3. Determine advanced machine learning techniques and the problem domain where these can be applied along with their performance analysis.
  4. Interpret & evaluate the results of models learned
  5. Apply gained knowledge to research in Machine Learning.
  6. Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures,

Modeling, Discussions, implementing enquiry based learning.

 

Learning activities for the students:

Experiential Learning, Presentations, Case based learning, Discussions, Quizzes and Assignments

 

  • Assignments
  • Written test in classroom
  • Classroom activity
  • Continues Assessment
  • Semester End Examination

 

12.00
Unit I: 
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.

12.00
Unit II: 
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

12.00
Unit III: 
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

12.00
Unit IV: 
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.

12.00
Unit V: 
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.

 

ESSENTIAL READINGS: 

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

REFERENCES: 

Suggested Readings:

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

E-Resources

  1. Introduction to Machine Learning, By NPTEL, https://nptel.ac.in/courses/106106139
  2. Machine Learning, By Coursera, https://www.coursera.org/learn/machine-learning
  3. 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
  4. Introduction to Machine Learning, By Udacity, https://www.udacity.com/course/intro-to-machine-learning--ud120                                                                                                                                                                                                                                                                              Journals
    1.Machine Learning with Applications, By Elsevier, https://www.journals.elsevier.com/machine-learning-with-applications
    2.Journal of Machine Learning Research, By Microtome publishing, http://www.mtome.com/Publications/JMLR/jmlr.html
    3.IEEE Transactions on Neural Networks and Learning Systems, https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385
    4.Machine Learning, By Springer, https://www.springer.com/journal/10994                                                                                                                                    

     

 

 

Academic Year: 
2024-25
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