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Course Detail

Course Name Fundamentals of Machine Learning
Course Code 19EAC301
Program B. Tech. in Electronics and Computer Engineering
Semester 5
Year Taught 2019


Module I

Introduction to Machine learning: Supervised learning, Unsupervised learning, The five tribes in Machine Learning, linear classification, perceptron update rule, Perceptron convergence, generalization, Maximum margin classification, Classification errors, regularization, logistic regression, Linear regression, estimator bias and variance, active learning

Module II

Non-linear predictions, kernels, Kernel regression, kernels, Support vector machine (SVM) and kernels, kernel optimization.

Module III

Model selection, Model selection criteria, Description length, feature selection, Combining classifiers, boosting, Bagging, margin, and complexity, Margin and generalization (EM) algorithm, EM, regularization, clustering, Clustering, Spectral clustering, Markov models, Hidden Markov models (HMMs), Bayesian networks, Learning Bayesian networks, Probabilistic inference, Current problems in machine learning.

Objectives and Outcomes

Course Objectives

  • To introduce mathematical methods for design of machine learning algorithms.
  • To provide an overview of cluster analysis process and cluster quality evaluation techniques.
  • To enable design and performance evaluation of classifiers for typical classification problems.
  • To enable design of frequent itemset mining system for typical solve market-basket analysis problems.

Course Outcomes

  • CO1: Able to generate, analyze and interpret data summaries.
  • CO2: Able to carry out analysis on machine learning algorithms.
  • CO3: Able to design and implement classifiers for machine learning applications.
  • CO4: Able to design and implement frequent itemset mining systems.

CO – PO Mapping

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 3 3
CO2 3 2 2 3
CO3 2 3 2 2 3
CO4 2 3 2 2 3

Textbook / References


  • Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995


  • Duda, Richard, Peter Hart, and David Stork, “Pattern Classification” Second Edition, New York, NY: WileyInterscience, 2000.
  • Hastie, T., R. Tibshirani, and J. H. Friedman, “The Elements of Statistical Learning: Data Mining, Inference and Prediction”, New York, Springer, 2001.
  • MacKay, David, “Information Theory, Inference, and Learning Algorithms”, Cambridge University Press, 2003.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

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