Machine Learning Techniques

CP7253 MACHINE LEARNING TECHNIQUES L T P C 3 0 2 4
OBJECTIVES
 To understand the concepts of machine learning
 To appreciate supervised and unsupervised learning and their applications
 To understand the theoretical and practical aspects of Probabilistic Graphical Models
 To appreciate the concepts and algorithms of reinforcement learning
 To learn aspects of computational learning theory
UNIT I INTRODUCTION 8+6
Machine Learning – Machine Learning Foundations –Overview – Design of a Learning system – Types of machine learning –Applications Mathematical foundations of machine learning – random variables and probabilities – Probability Theory – Probability distributions -Decision Theory- Bayes Decision Theory – Information Theory
UNIT II SUPERVISED LEARNING 10+6
Linear Models for Regression – Linear Models for Classification – Naïve Bayes – Discriminant Functions -Probabilistic Generative Models -Probabilistic Discriminative Models – Bayesian Logistic Regression. Decision Trees – Classification Trees- egression Trees – Pruning. Neural Networks -Feed-forward Network Functions – Back- propagation. Support vector machines – Ensemble methods- Bagging- Boosting
UNIT III UNSUPERVISED LEARNING 8+6
Clustering- K-means – EM Algorithm- Mixtures of Gaussians. The Curse of Dimensionality -Dimensionality Reduction – Factor analysis – Principal Component Analysis – Probabilistic PCA- Independent components analysis
UNIT IV PROBABILISTIC GRAPHICAL MODELS 10+6
Graphical Models – Undirected graphical models – Markov Random Fields – Directed Graphical Models -Bayesian Networks – Conditional independence properties – Inference – Learning- Generalization – Hidden Markov Models – Conditional random fields(CRFs)
UNIT V ADVANCED LEARNING 9+6
Sampling –Basic sampling methods – Monte Carlo. Reinforcement Learning- K-Armed Bandit- Elements – Model-Based Learning- Value Iteration- Policy Iteration. Temporal Difference Learning- Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions Computational Learning Theory – Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting TOTAL : 45 + 30 : 75 PERIODS 20
OUTCOMES:
Upon completion of this course, the student should be able to
 Design a neural network for an application of your choice
 Implement probabilistic discriminative and generative algorithms for an application of your choice and analyze the results
 Use a tool to implement typical clustering algorithms for different types of applications
 Design and implement an HMM for a sequence model type of application
 Identify applications suitable for different types of machine learning with suitable justification
REFERENCES:
1. Christopher Bishop, “Pattern Recognition and Machine Learning” Springer, 2007.

Bishop – Pattern Recognition And Machine Learning – Springer 2006
2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.
3. Ethem Alpaydin, “Introduction to Machine Learning”, MIT Press, Third Edition, 2014.

Introduction_to_Machine_Learning_-_2e_-_Ethem_Alpaydin
4. Tom Mitchell, “Machine Learning”, McGraw-Hill, 1997.
5. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning”, Springer, Second Edition, 2011.
6. Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, Chapman and Hall/CRC Press, Second Edition, 2014.

Notes:

[Chapman & Hall_Crc Machine Learning & Pattern Recognition] Stephen Marsland – Machine Learning_ An Algorithmic Perspective, Second Edition (2014, Chapman and Hall_CRC)

06-VectorSpaceModel (1)

clustering

L03_Decision_Trees

M.L, A.C.D, I.R.T.Qpapers

PAGE NO

probir

Reinforcement Learning

ReinforcementLearningExample

Sampling

Stefan Büttcher, Charles L. A. Clarke, Gordon V. Cormack – Information Retrieval. Implementing and Evaluating Search Engines (2010, MIT)