Tools from machine learning are now ubiquitous in the sciences with applications in engineering, computer vision, and biology, among others. This class introduces the fundamental mathematical models, algorithms, and statistical tools needed to perform core tasks in machine learning. Applications of these ideas are illustrated using programming examples on various data sets.
Topics include pattern recognition, PAC learning, overfitting, decision trees, classification, linear regression, logistic regression, gradient descent, feature projection, dimensionality reduction, maximum likelihood, Bayesian methods, and neural networks.
Mistake Bounded Learning (1 week)
Decision Trees; PAC Learning (1 week)
Cross Validation; VC Dimension; Perceptron (1 week)
Linear Regression; Gradient Descent (1 week)
Boosting (.5 week)
PCA; SVD (1.5 weeks)
Maximum likelihood estimation (1 week)
Bayesian inference (1 week)
K-means and EM (1-1.5 week)
Multivariate models and graphical models (1-1.5 week)
Neural networks; generative adversarial networks (GAN) (1-1.5 weeks)
Linear Algebra, Probability, Experience programming in Python