Machine Learning

Code: MA3172 | L-T-P-C: 3-0-3-9

Introduction to learning: supervised and unsupervised, generative and discriminative models, classification and regression problems, performance measures, design of experiments; Feature space and dimensionality reduction: Feature selection, PCA, exploratory factor analysis, LDA, ICA; Unsupervised learning: K-means clustering, hierarchical agglomerative clustering, DBSCAN, MLE, MAP, Bayesian learning, Gaussian Mixture Models; Supervised learning: Bayesian decision theory, Logistic Regression, data balancing, simple perceptron and multi-layer perceptron, Parzen windows, k-nearest neighbor, decision trees, support vector machines; ensemble methods, bagging and boosting; Applications and case studies.

Practical: Sci-kit Learn, NumPy and MatPlotLib; PCA and LDA; K-means Clustering, Hierarchical Agglomerative Clustering and DBSCAN; MLE and Bayesian learning; Linear and Logistic Regression; Perceptron; Data Balancing & Imbalance-Learning; Multi-layer perceptron; k-nearest neighbor, Classification and Regression Trees; Support Vector Machines; Random Forest, AdaBoost.

Texts:

• Ethem Alpaydin, Introduction to Machine Learning, Third Edition, Prentice Hall of India, 2015.
• Tom M. Mitchell, Machine Learning, McGraw Hill Education, 2017

 

References:

• C. M. Bishop, Pattern Recognition and Machine Learning, Second Edition, 2011.
• Miroslav Kubat, An Introduction to Machine Learning, Third Edition, Springer, 2021.
• S. O. Haykin, Neural Networks and Learning Machines, Third Edition, Pearson Education, 2016.