CSE176 Introduction to Machine Learning (Fall semester 2015)


Miguel Á. Carreira-Perpiñán
Electrical Engineering and Computer Science
School of Engineering
University of California, Merced
mcarreira-perpinan-[at]-ucmerced.edu; 209-2284545
Office: 217, Science & Engineering Building 2

Office hours: Mondays/Wednesdays 2:45-3:45pm (SE2-217).

TA: Guoxiang Zhang, gzhang8-[at]-ucmerced.edu. TA hours: Wednesdays 11am-12pm (AOA142).

Lectures: Mondays/Wednesdays 1:30-2:45pm (COB114).

Lab class: Fridays 1:30-4:20pm (Linux Lab, SE1-138).

Course web page: http://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176

Course description

Survey of techniques for the development and analysis of software that learns from experience. Specific topics include: supervised learning (classification, regression); unsupervised learning (clustering, dimensionality reduction); reinforcement learning; computational learning theory. Specific techniques include: Bayesian methods, mixture models, decision trees, instance-based methods, neural networks, kernel machines, ensembles, and others.

Prerequisites: CSE31, MATH24, MATH32. Essentially, you need to know the fundamentals of linear algebra, multivariate calculus and probability, and have good programming skills.


Required textbook (get the errata and additional errata):

The companion site for the book has additional materials (lecture slides, errata, etc.).

Other books recommended as additional reading at undergraduate level:

If you want to refresh your knowledge of linear algebra, multivariate calculus or probability, the following are helpful (any edition or similar book is fine):

The textbook has an appendix on probability. Some of the other books have similar appendices on linear algebra, multivariate calculus or probability.

Syllabus and required textbook reading


Before each class, you should have read the corresponding part of the textbook and the notes. I will teach the material in the order below (which is more or less the order in the book).

Textbook reading (table of contents):


The notes to accompany the textbook (bring the corresponding part to each class):
Carreira-Perpiñán, M. Á. (2015): CSE176 Introduction to Machine Learning: Lecture notes. University of California, Merced, 2015.


Course grading

While I encourage you to discuss your work with other students, the homeworks, lab assignments, project and exams must be the result of your own work without collaboration. See the Academic Dishonesty Statement and the UC Merced Academic Honesty Policy.

Grade curves (exams): final.

Matlab tutorials

Matlab tutorials: if you have never used Matlab, there are many online tutorials, for example:

Also see Matlab courses from Engineering Service Learning at UC Merced.


Miguel A. Carreira-Perpinan
Last modified: Wed Jun 22 20:45:09 PDT 2016

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