This is an inactive course webpage. Find the one for your current semester.
For information about CSE517a in spring 2017 go HERE.
No project in the last week of class. If you interested in applying the learned concepts to real world problems and data challenges check out Kaggle competitions.
Here is the project leaderboard.
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 247 and sufficient mathematical maturity (matrix algebra, probability theory / statistics, multivariate calculus, basic approaches in optimization). Knowledge of Matlab/Octave.
Instructor: Marion Neumann
Office: Jolley Hall Room 403
Office Hours: MON 4-5pm (not on 2nd of May), TUE 3rd of May 1-2pm
Please ask any questions related to the course materials and homework problems on Piazza. Other students might have the same questions or are able to provide a quick answer. Any postings of solutions to problems (written or in form of source or pseudo code) will result in a grade of zero for that particular problem for ALL students.
TA Office Hours
Grades on BB
Resources and HowTos
Enrollment/Waitlist: Enrollment for this course will NEITHER consider your time of enrollment NOR your place on the waitlist. The instructor will hold a take-home placement exam (on basic mathematical knowledge) that is due on the first day of class (submit a paper copy). Enrollment will be solely based on the performance in this test. Only students who pass the placement exam will be enrolled in the course. Download the placement test HERE.
Do not email me with questions about the logistics OR contents of this test.
Example Resources for preparing for the course (and placement test) are:
- the first three weeks of Andrew Ng’s online course on machine learning (The first week gives a good general overview of machine learning and the third week provides a linear-algebra refresher.)
- The Matrix Cookbook (this is a collections of facts regarding matrices, linear algebra, statistics and probability, multivariate distributions, and other topics)
- Learning from Data (LFD) by Abu-Mostafa, Magdon-Ismail, and Lin (this book will not be used as course book, however, it is a good resource!)
Course Books: The main book is Kevin Murphy Machine Learning A Probabilistic Perspective. A useful reference book is Hastie, Tibshirani, Friedman The Elements of Statistical Learning.