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CSE517a: Machine Learning [Spring 2015]

Associate Prof. Kilian Weinberger

Course Number: CSE 517a
Credit: 3 Units
Times: 2:30pm-4:00pm Tuesdays and Thursdays (01/13/15 - TBA)
Room: Louderman 458
Midterm Exam: TBA
Final Exam: May 6th, 3:30pm
Teaching Assistants: Yu Sun, Nick Kolkin, Charles Schaff, Maxwell Wang
Office Hours: Wednesday 9am-10am Jolley 407
Recitations: FR 9am-10am Jolley 407
Piazza: Piazza Message board
Leaderboards: Project1, Project2

- You have to pass the take-home placement exam. Please bring your exam with you to the first lecture.
- 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.
- Mathematical maturity and experience
- Knowledge of Matlab. If you are unfamiliar with Matlab, please consider taking CSE 200 or work through the first few weeks of Andrew Ng's online course on machine learning.

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.

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.

This course will teach you (amongst other things):
- Parametric / Non-parametric learning
- Bias/Variance Trade-off
- Boosting
- Support Vector Machines
- Deep Learning
- Bayesian vs. frequentist learning
- Unsupervised learning
- Recommender systems

Course Books:
The main book is Kevin Murphy Machine Learning A Probabilistic Perspective. As reference book we will also use Hastie, Tibshirani, Friedman The Elements of Statistical Learning.

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