Syllabus
- Introduction: what is machine learning, supervised, reinforcement and unsupervised learning
- Supervised learning/prediction: types of predictors, bias-variance tradeoff, overfitting, curse of dimensionality
- Families of predictors for regression and classification: linear and logistic regression, nearest neighbor classifiers, regression and decision trees, neural networks, boosted predictors
- Regularization: Lasso and sparsity, Support Vector Machines and the kernel trick
- Learning theory
- Model selection
- Unsupervised learning problems, [semisupervised learning]
- Clustering, mixtures distributions, K-means and EM algorithms
- [Non-parametric clustering], spectral clustering
- [Linear and non-linear dimension reduction ]
- [Graphical models basics]
- [Markov decision processes and reinforcement learning]
topics in [] are optional, time permitting
|