Tejasvi Kothapalli
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Course Review

UC Berkeley's Machine Learning Curriculum Review

A chronological overview of Berkeley ML-related coursework I took, plus a brief perspective on the broader Berkeley learning experience.

Author Tejasvi Kothapalli
Affiliation UC Berkeley
Published November 20, 2022
Tags Course Review, Berkeley, Machine Learning, EECS

Preface

These course reviews were inspired by Daniel Seita's blog. When I was an undergraduate at UC Berkeley, I often found myself reading through his course reviews (for example CS 280, CS 288) when deciding which grad courses to take.

These are the upper-division machine learning courses (or at least courses I feel are related to machine learning education) I took while at UC Berkeley, listed in chronological order, with semester and grade. If you are interested in understanding the hierarchy (natural order to take these courses), HKN has a helpful guide.

Course list (chronological)

  • Math 110, Linear Algebra, Sophomore Fall 2019, A-
  • EECS 126, Probability and Random Processes, Sophomore Spring 2020, P
  • EECS 127, Optimization Models in Engineering, Sophomore Spring 2020, A-
  • CS 100, Principles & Techniques of Data Science, Sophomore Summer 2020, A
  • CS 188, Introduction to Artificial Intelligence, Sophomore Summer 2020, A-
  • CS 189, Introduction to Machine Learning, Junior Fall 2020, A
  • CS 182, Designing, Visualizing and Understanding Deep Neural Networks, Junior Spring 2021, A-
  • EE 120, Signals and Systems, Junior Spring 2021, P
  • CS 194-80, Full Stack Deep Learning, Junior Spring 2021, A
  • CS 194-26, Intro to Computer Vision and Computational Photography, Senior Fall 2021, A
  • CS 285, Deep Reinforcement Learning, Decision Making, and Control, Senior Fall 2021, A
  • CS 280, Computer Vision, Senior Spring 2022, A
  • CS 288, Natural Language Processing, Senior Spring 2022, A
  • CS H196A, Senior Honors Thesis Research, Senior Spring 2022, A+

My overall thoughts on education at Berkeley

I think it helps readers to understand the perspective from which I wrote these course reviews, so they can make their best judgment and decision.

I am a huge advocate for Berkeley's course experience, especially for machine learning. However, Berkeley's educational experience is not limited to courses. Research, clubs, internships, and many other paths also matter. Just because I am a huge proponent of these courses does not necessarily mean that everyone will enjoy or learn from them to the same extent.