CP5191 MACHINE LEARNING TECHNIQUES SYLLABUS - ANNA UNIVERSITY PG REGULATION 2017

OBJECTIVES:

• To introduce students to the basic concepts and techniques of Machine Learning.
• To have a thorough understanding of the Supervised and Unsupervised learning techniques
• To study the various probability based learning techniques
• To understand graphical models of machine learning algorithms

CP5191 MACHINE LEARNING TECHNIQUES SYLLABUS

UNIT I INTRODUCTION
Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear Regression.

UNIT II LINEAR MODELS
Multi-layer Perceptron – Going Forwards – Going Backwards: Back  Propagation Error – Multi- layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back- Propagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality –  Interpolations and Basis Functions – Support Vector Machines.

UNIT III TREE AND PROBABILISTIC MODELS
Learning with Trees – Decision Trees – Constructing Decision Trees –  Classification and Regression Trees – Ensemble Learning – Boosting – Bagging – Different ways to Combine Classifiers – Probability and Learning – Data into Probabilities – Basic Statistics – Gaussian Mixture Models – Nearest Neighbor Methods – Unsupervised Learning – K means Algorithms –
Vector Quantization – Self Organizing Feature Map

UNIT IV DIMENSIONALITY REDUCTION AND EVOLUTIONARY MODELS
Dimensionality Reduction – Linear Discriminant Analysis – Principal Component Analysis – Factor Analysis – Independent Component Analysis – Locally Linear Embedding – Isomap – Least Squares Optimization –  Evolutionary Learning – Genetic algorithms – Genetic Offspring: - Genetic Operators – Using Genetic Algorithms – Reinforcement Learning – Overview – Getting Lost Example – Markov Decision Process

UNIT V GRAPHICAL MODELS
Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution –  Markov Chain Monte Carlo – Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models – Tracking Methods

TOTAL: 45 PERIODS

OUTCOMES:

• Upon completion of this course, the students will be able to:
• Distinguish between, supervised, unsupervised and semi-supervised learning
• Apply the appropriate machine learning strategy for any given problem
• Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem
• Design systems that uses the appropriate graph models of machine learning
• Modify existing machine learning algorithms to improve classification efficiency

REFERENCES:

1. Ethem Alpaydin, ―Introduction to Machine Learning 3e (Adaptive  Computation and Machine Learning Series)‖, Third Edition, MIT Press, 2014
2. Jason Bell, ―Machine learning – Hands on for Developers and Technical Professionals‖, First Edition, Wiley, 2014
3. Peter Flach, ―Machine Learning: The Art and Science of Algorithms that Make Sense of Data‖, First Edition, Cambridge University Press, 2012.
4. Stephen Marsland, ―Machine Learning – An Algorithmic Perspective‖, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
5. Tom M Mitchell, ―Machine Learning‖, First Edition, McGraw Hill  Education, 2013.