Erik Cheng | teaching

Thus far, I have only taught courses at UC Berkeley. If you’re a current student and have ended up here somehow, I would suggest looking for more updated materials, as I have not been at Cal since Spring 2018. However, I have left this up in case anyone still finds use from these materials.

UC Berkeley

Data 8 (CS/STAT/INFO 8)

Slides:

Other useful stuff:

My complete guide to all things probability (through the midterm).
Materials from Spring 2017's tutoring sessions
- To use these notebooks, you'll need to grab the files from Github. I'd suggest forking the repo, but you can individually download each file using this button(make sure you download every file that's in the folder for the week you're interested in if you do). For both methods, I think you need to install Github for desktop. Then, you can upload these files to datahub (ideally in a new folder). If you don't have access to datahub, you'll have to do this through a local installation of Jupyter. I'd recommend using Anaconda for this. Let me know if you run into any issues getting these files. Getting started with Github,
Data 8's main website, textbook, resources tab, and documentation.
A great guide for introductory probability and statistics; this is actually the textbook for Stat 88. A great supplement to what's already covered in the main textbook, and has some further topics for those interested.
Visualizing Statistics
Midterm walkthrough

UC Berkeley

Rubik's Cube Decal

OLL:

Refer to this page for OLL algorithms.

To get to this point (ie the yellow cross is solved), remember that application of the algorithm F(RUR'U')F' is necessary.

BEGINNERS: Memorize the second algorithm under "All Edges Oriented".

ADVANCED: Memorize all algorithms under "All Edges Oriented".

PLL:

Refer to this page for PLL algorithms.

BEGINNERS: Memorize Aa under "Opposite Edge Swap".

ADVANCED: Memorize all algorithms under EPLL, as well as Aa under "Opposite Edge Swap" and E under "Diag corner swap".