Course Preview

**Machine Learning**

**Instructor**: Kilian Weinberger

**Department**: Computer Science

**Institution**: Cornell University

**Platform**: Independent

**Year**: 2017

**Price**: Free

**Prerequisites**:

Mathematical maturity and experience Students interested in preparing for the exam are advised to work through the first three weeks of Andrew Ng's online course on machine learning.

**Textbook**:

Kevin P. Murphy. Machine Learning: A Probabilistic Perspective.

T. Hastie, R. Tibshirani, and J. Friedman: "Elements of Statistical Learning", Springer-Verlag, 2001.

**Description**:

A. Objective: The goal of this course is to give an introduction to the field of machine learning. The course will teach you basic skills to decide which learning algorithm to use for what problem, code up your own learning algorithm and evaluate and debug it. B. Abstract: The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. Recently, many successful machine learning applications have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to information-filtering systems that learn users' reading preferences, to autonomous vehicles that learn to drive. There have also been important advances in the theory and algorithms that form the foundation of this field. This course will provide a broad introduction to the field of machine learning. Prerequisites: CSE 241 and sufficient mathematical maturity (Matrix Algebra, probability theory / statistics, multivariate calculus). There is no enrollment limit, but the instructor will hold a take-home placement exam (on basic mathematical knowledge) that is due on the first day of class. C. The course will cover the following topics: 1. Overview (What is machine learning) 2. k-nearest neighbors 3. The Perceptron 4. Estimating Probabilities from Data 5. Naive Bayes 6. Logistic Regression 7. Gradient Descent 8. Linear Regression 9. Linear SVM 10. Empirical Risk Minimization 11. Bias / Variance Tradeoff 12. ML Debugging, Over- / Underfitting 13. Kernel Machines I 14. Kernel Machines II 15. Gaussian Processes / Bayesian Global Optimization 16. Fast nearest neighbor search 17. Decision / Regression Trees 18. Bagging 19. Boosting 20. Deep Learning