This will be a 4-unit course with programming assignments, two midterms and a project. There is no final exam.
Content will be similar to the offering in Fall 2016.
The graduate (294) and undergraduate (194) versions differ in the content of the project, with additional content required for CS294. This will be explained in the first lecture.
Content will be streamed and webcast.
The prerequisites for this course are:
* Knowledge of calculus and linear algebra, Math 53/54 or equivalent. You'll need this throughout the course.
* Probability and Statistics, CS70 or Stat 134. We'll talk about continuous and discrete probability distributions. CS70 is bare minimum preparation, a stat course is better.
* Machine Learning, CS189. You can probably handle this course without a machine learning course before, but it will be very helpful.
* Programming, CS61B or equivalent. Assignments will mostly use Python. If you need some help, try this tutorial from Stanford's CS231n