Subject Code: ID6L004

Name: Machine Learning and Data Analytics-I

L-T-P: 3-0-0

Credit: 3

Prerequisite: None

Introduction: Fixed, Adaptive and Intelligent Systems; Adaptive Techniques: Prediction, Classification, Forecasting, Filtering, Direct and Inverse modeling.
Data Exploration and Pre-processing: Data Objects and Attributes; Statistical Measures, Visualization, Data Cleaning and Integration.
Dimensionality Reduction: Linear Discriminant Analysis; Principal Component Analysis, Independent Component Analysis; Transform Domain and Statistical Feature Extraction and Reduction.
Regression: Least Mean Square and Recursive Least Square Algorithms; and Support Vector Machine.
Clustering: K-Means, Hierarchical, and Density-based Clustering, Spectral Clustering.
Classification: Decision Tree Induction including Attribute Selection, and Tree Pruning, Random Forests, Support Vector Machine, Ensemble Classification
Artificial Neural Networks: Single Layer Neural Network, Multilayer Perceptron, Back Propagation Learning, Functional Link Artificial Neural Network, and Radial Basis Function Network, Recurrent Neural Networks, Deep Learning, Convolutional Neural Networks.
Association Analysis: Frequent Itemset Generation and Rule Generation, Apriori Algorithms
Time Series Analysis: Time Series clustering, Time series alignment, Dynamic Time Warping
Bio-Inspired Techniques: Genetic Algorithm, Schemata Theorem, Differential Evolution, Particle Swarm Optimization, Ant Colony Optimization, Convergence Analysis.

1.

Bishop, C., Pattern Recognition and Machine Learning, Springer, 2006.

2.

Daumé, H. III, A Course in Machine Learning, 2015 (freely available online)

3.

Hastie, T., R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer 2009 (freely available online)

4.

Haykin S., Neural Networks and Learning Machines, Third Edition, Prentice Hall, 2008. Goodfellow I.,Bengio Y. and Courville A.; Deep Learning, MIT Press, 2016

5.

Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.

6.

NPTEL lectures on Introduction to Machine Learning.