CSE342: Statistical Machine Learning is a 3xx-level course offered to undergrads pursuing the CSE and CS+X disciplines at IIIT-Delhi. The course focuses on the topics in statistical theory that are important for researchers in machine learning, including nonparametric theory, consistency, minimax estimation, and concentration of measure using statistical techniques to develop models that can learn from data and make predictions or decisions.
Topics Covered:
- Bayes Decision theory and Error bounds (Chernoff, Bhattacharya, Hoeffding)
- Feature extraction (FDA, PCA)
- Parameter estimation (MLE/MAP)
- Linear classifier (Discriminant analysis, LDA, QDA)
- Gaussian processes (Regression)
- Foundations of Deep learning (FFNN, weight decay, regularization) [overlaps with DL]
- Bagging (reducing var)
- Boosting (reducing bias) – AdaBoost, Gradient Boosting
- Clustering (spectral clustering, min/ratio cut)
- Applications (time permitting)