AL3451 MACHINE LEARNING Anna University Syllabus R2021

 

 

AL3451 MACHINE LEARNING  Anna University Syllabus R2021

AL3451                       MACHINE LEARNING                  L T P C3003

COURSE OBJECTIVES:

•  To understand the basic concepts of machine learning.

•  To understand and build supervised learning models.

•  To understand and build unsupervised learning models.

•  To evaluate the algorithms based on corresponding metrics identified

UNIT I                             INTRODUCTION TO MACHINE LEARNING               8

Review of Linear Algebra for machine learning; Introduction and motivation for machine learning;
Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably
Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization, Bias
variance trade-off.

UNIT II                                SUPERVISED LEARNING                         11

Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression,
gradient descent, Linear Classification Models: Discriminant function – Perceptron algorithm,
Probabilistic discriminative model - Logistic regression, Probabilistic generative model – Naive
Bayes, Maximum margin classifier – Support vector machine, Decision Tree, Random Forests

UNIT III                      ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING              9

Combining multiple learners: Model combination schemes, Voting, Ensemble Learning - bagging,
boosting, stacking, Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian
mixture models and Expectation maximization.

UNIT IV                                     NEURAL NETWORKS                              9

Multilayer perceptron, activation functions, network training – gradient descent optimization –
stochastic gradient descent, error backpropagation, from shallow networks to deep networks –Unit
saturation (aka the vanishing gradient problem) – ReLU, hyperparameter tuning, batch
normalization, regularization, dropout.

UNIT V               DESIGN AND ANALYSIS OF MACHINE LEARNING EXPERIMENTS          8

Guidelines for machine learning experiments, Cross Validation (CV) and resampling – K-fold CV,
bootstrapping, measuring classifier performance, assessing a single classification algorithm and
comparing two classification algorithms – t test, McNemar’s test, K-fold CV paired t test

COURSE OUTCOMES:

At the end of this course, the students will be able to:
CO1: Explain the basic concepts of machine learning.
CO2 : Construct supervised learning models.
CO3 : Construct unsupervised learning algorithms.
CO4: Evaluate and compare different models
TOTAL:45 PERIODS

TEXTBOOKS:

1. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Fourth Edition, 2020.
2. Stephen Marsland, “Machine Learning: An Algorithmic Perspective, “Second Edition”, CRC
Press, 2014.

REFERENCES:

1. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
2. Tom Mitchell, “Machine Learning”, McGraw Hill, 3rd Edition, 1997.
3. Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, “Foundations of Machine
Learning”, Second Edition, MIT Press, 2012, 2018.
4. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016
5. Sebastain Raschka, Vahid Mirjalili , “Python Machine Learning”, Packt publishing, 3rd
Edition, 2019.