Syllabus
Introduction to Machine Learning: Capacity, overfitting and underfitting, regularization techniques, hyperparameters, bias and variance, PAC model, Rademacher complexity, growth function, VC-dimension, model evaluation and selection, ML frameworks in python.
Supervised Learning: Concepts of classification and regression, linear and ridge regression, perceptron, k-nearest neighbor classifiers, decision tree, logistic regression, naive Bayes, Gaussian discriminant analysis, linear models for classification and regression, multi-class classification techniques.
ML Architectures: Kernel methods: SVM, Neural networks: multilayer perceptron, Graphical Models: Hidden Markov Models (HMM). Unsupervised Learning: Cluster analysis, k-means, hierarchical clustering, spectral clustering, mixture modeling, self-organizing maps, independent component analysis (ICA). Dimensionality Reduction Methods: Supervised feature selection, principal component analysis.
Ensemble Learning: Bagging, boosting, AdaBoost, random forest. Data Preprocessing: Outlier mining; imbalance problem. Learning Techniques: Introduction to semi-supervised learning, reinforcement learning, transfer learning, active learning.
Text Books
References
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0262035613.
- Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. ISBN: 978-0262018029.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. ISBN: 978-0387310732.
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. ISBN: 978-0387848570.
- Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd Edition). Morgan Kaufmann.