Subsections
[Cr:4, Lc:3, Tt:1, Lb:0]
- Bayesian Learning: Random variables, Probability functions, Canonical probability distributions, Joint probability distribuion, Bayes rule, Independence, Bayesian belief networks, Naive bayes, Markov models, Hypothesis testing, Overfitting.
- Diffeent measures in classification/ regression: Precision, recall AUC, MAPE.
- Supervised Learning: Decision trees, SVM, Lasso and Ridge regression, Ensemble models.
- Unsupervised Learning: EM, K-means, Hierarchical clustering, Birch, Spectral clustering.
- Introduction to Neural networks.
- Project: Implement a multi-layered perceptron using numpy-application to character recognition, Modelling employee churn.
- Jerome Friedman, Trevor Hastie and Robert Tibshirani, The Elements of Statistical Learning, Data Mining, Inference and Prediction, Second Edition, Springer Series in Statistics.
- Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press.
- Tom M. Mitchell, Machine Learning, McGraw Hill Eduction.
- Alpaydin Ethem, Introduction to Machine Learning, PHI Learning Pvt. Ltd.
