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Review of Berkeley Classes

I wanted to write this post becuase back when I was an underclassmen and confused about what classes to take, I really scoured the web for reviews of certain classes and professors and would’ve found such posts immensely helpful. However, I must give the disclaimer that these reviews are based on just my own experiences and biases and may not be representative of the class experience for other people. Furthermore, it’s been several years since I’ve taken some of these classes so my memory is a little hazy. I will be reviewing (mostly) STEM classes as I feel most qualified to judge those, and I think my course plan follows that of a student interested in ML.

Fall 2017

CS 61A - SICP by John DeNero

Overall: 6/10 By this point, there are literally tens of thousands of students that have taken this class, and the class itself is a pretty well-oiled machine. I personally didn’t like the class relative to other CS classes because I thought that the test questions were incredibly contrived though I understand the necessity. Given how big tests are weighed in CS classes, this was a big factor for me. Other than that, I enjoyed the projects and other aspects of the class and had to take it for my major.

EE 16A - Designing Information Devices and Systems I by Anant Sahai and Elad Alon

Overall: 6/10 All I can recall

Physics 5B - Introductory Electromagnetism, Waves, and Optics by Matt Pyle

Overall: 7/10

Physics 5BL - Introduction to Experimental Physics I by Achilles Speliotopoulos

Overall: 6/10

Math 53 - Multivariable Calculus by Edward Frenkel

Overall: 5/10

MCB 166 - Biophysical Neurobiology by Alan Miller, Tamira Elul

Overall: 7/10 I don’t exactly remember why I decided to take this class; I think I was still entertaining the idea of pre-med freshman fall and a quantitative physics-based biology class seemed interesting enough. The class consisted of six homework assignments and a take-home midterm and final where most questions involved some quantitative modelling of a nervous system (generally into a differential equation) and then observing the behavior of your model. I actually found the material very interesting, and it goes into a lot of Nobel-winning work that I never knew about. As it’s most likely that people who read this are CS-focused, no, I don’t think this helped me at all in any connections between biology and neural networks, but it was interesting nevertheless. In another life, I might’ve gone further into this field. One negative experience I have about this class though, was that I don’t think I interacted with a single other student throughout the entire class. As I wasn’t a bio student and I don’t think anyone else was a freshman, I didn’t really have any collaborators for the class which made it less enjoyable than it could’ve been.

Spring 2018

CS 61B - Data Structures and Algorithms by Josh Hug

CS 70 - Discrete Mathematics and Probability Theory by Satish Rao and Babak Ayazifar

EE 16B - Designing Information Devices and Systems II by Michel Maharbiz and Jaijeet Roychowdhury

Physics 5C - Introductory Thermodynamics and Quantum Mechanics by Dan Kasen

Physics 5CL - Introduction to Experimental Physics II by Gurpreet Kaur

Math 110 - Linear Algebra by Edward Frenkel

Fall 2018

CS 61C - Great Ideas in Computer Architecture by Dan Garcia

CS 170 - Efficient Algorithms and Intractable Problems by Satish Rao and Alessandro Chiesa

EE 126 - Probability and Random Processes by Kannan Ramchandran and Abhay Parekh

Physics 110B - Electromagnetism and Optics by Ori Ganor

Physics 137B - Quantum Mechanics II by Lawrence Hall

Spring 2019

CS 162 - Operating Systems and System Programming by John Kubiatowicz

CS 189 - Introduction to Machine Learning by Jonathan Shewchuk

CS 270 - Combinatorial Algorithms by Satish Rao

Physics 105 - Analytic Mechanics by Stuart Bale

Econ 101A - Microeconomic Theory by Yuan Tang

Fall 2019

CS 285 - Deep Reinforcement Learning by Sergey Levine

EE 229A - Information Theory by Thomas Courtade

Stat 210A - Theoretical Statistics by Will Fithian

Physics 151 - QFT Primer by Hitoshi Murayama