CS [L,W]182/282A Designing, Visualizing and Understanding Deep Neural Networks Spring 2020

New YouTube Video Location:

The class will be recorded and available on youtube. Here are the new coordinates:
Lecture Playlist: https://www.youtube.com/playlist?list=PLnocShPlK-FvSQvoTWZuJQzEiDDAY64kT Links to an external site.

and new zoom lecture link Zoom 434-690-973 Links to an external site.

Important: New Course Format

This semester, 182/282A will be a new format. The "L" section has live lectures with mandatory attendance. The "W" section has only recorded or live-streamed lectures. Students in the "W" section should not attend the "L" section lectures. The lectures themselves are identical. The rest of the course material, assignments, midterms, discussion sections, Piazza etc. are identical.

iClickers

If you're in one of the in-class sections, please acquire an iClicker (any model from iCLicker+, iClicker2 etc will work) and register it (the registration link is on the left) by class on 2/4/20. If you're not in a live section, you dont need an iClicker (for grads you are not in a live section unless you requested to be).

Course Information

Lectures: 9:30am-11am TuTh in 306 Soda. Please attend lecture (including the first one) only if you are enrolled in the "L" section of 182. The "W" section and 282A are online only.

Reporting Issues

Please read this page before posting.

Piazza

Use this link (Links to an external site.) to Piazza for general class discussions

Logistics

• Course Numbers: CS [L,W]182/282A Spring 2020, UC Berkeley
• Class Numbers:
  
• Instructor: John CannyLinks to an external site.
• GSIs:
  • To send an email with questions to all GSIs: cs182-gsi@lists.berkeley.edu 
  • David Chan: davidchan@berkeley.edu
  • Roshan Rao: roshan@berkeley.edu
  • Aravind Srinivas: aravind@cs.berkeley.edu
  • Haozhi Qi: hqi@eecs.berkeley.edu
  • Phillipe Laban: phillab@berkeley.edu
  • Forrest Huang: forrest_huang@berkeley.edu
• Time: 9:30am-11am, Tu-Th
• Location: 306 Soda
• Discussion: Join Piazza (Links to an external site.) for announcements and to ask questions about the course

Prerequisites

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 may be able to manage the course without 189 but in that case you should have a strong stat background.

* Programming, CS61B or equivalent. Assignments will mostly use Python. If you need some help, try this tutorial from CS231n (Links to an external site.)Links to an external site.

Texts

We'll frequently use the online book: Deep Learning (Links to an external site.)Links to an external site. by Ian Goodfellow and Yoshua Bengio and Aaron Courville. For reinforcement learning, the new version of Sutton and Barto's classic book is available online (Links to an external site.)Links to an external site..

Grading

• Class Participation: 10%
• Midterms: 30%
• Final Project (in groups): 30%
• Assignments : 30%

Slip Days

You can use up to 8 slip days for late assignments.

Office hours

All Office Hours + Discussion can also be found in this calendar link Links to an external site..

Lectures Online

The class will be recorded and available on youtube. Here are the new coordinates:
Lecture Playlist: Links to an external site.https://www.youtube.com/playlist?list=PLnocShPlK-FvSQvoTWZuJQzEiDDAY64kT Links to an external site. Links to an external site.

Please use this link for live streaming: zoom lectures Links to an external site.

In addition, the powerpoint slides posted *after* class will include audio and inline questions, and will be labelled "lecXX_voice.pptx". We recommend using the slides rather than the video to watch lectures.

Finally, there are pdf files without animation and without sound, however the embedded questionaire links still work.

Schedule