This course is both highly engaging and immensely useful. It covers a variety of optimization methods and problems, along with practical tools for learning, all presented in an interesting and dynamic manner. Initially, I was concerned after reading earlier reviews, but Tingwei turned out to be one of the best instructors I’ve ever had. She demonstrates an exceptional command of the course material and excels in teaching. The course is well-structured and logically coherent, with concepts clearly explained alongside their practical applications, such as in AI.

Tingwei is incredibly approachable during office hours, where you can discuss not only course-related topics but also career guidance. While she may appear serious during class, she is genuinely warm and supportive one-on-one. Her expertise in the field is remarkable, so I highly encourage students to ask her questions and seek her insights.

Assignments are thoughtfully designed, with around ten questions each week that, while time-consuming, are invaluable for mastering the material and sharpening thinking. The exams are straightforward and helpful, with the midterm averaging around 80 and the final around 90. This course was an incredible learning experience, and I feel lucky to have taken it, especially as it was Tingwei’s last time teaching optimization.

Math 164 covers content on techniques of minimizing/maximizing functions. Content from multivariable calculus and linear algebra are applied in this course, making this a computational heavy course. There are proofs needed but nothing as rigorous as Math 115a or Math 131a. Professor Meng spends most of the time covering optimization on quadratic functions of multiple dimensions but will go over other techniques that are used for machine learning. She has 10 homework assignments, one midterm, and one final. Overall, this class is basically a prep class for machine learning as many techniques learned here are used for many different forms of machine learning. Now for Professor Meng specifically:

Pros:
- Grading is very lenient. On homework, she grades it based off a select random collection of problems, but it seems like she's giving many lenient points even if you get the problem wrong. On exams, it's structured to have around 5 multiple choice questions and a free response section. On the final, I did not finish the frq and wrote random bs around, yet she gave me full marks on it. It seems like as long as you get the idea, she'll give points.
- Her class pacing is consistent. Never did it feel like we were going too fast/slow or behind/ahead. She stuck with her schedule well despite the occurrences she was gone and even during the protest week.
- She's very helpful in office hours. Note, she does NOT go over current homework during her office hours but she will take the time to explain solutions for past assignments. She also explains thoroughly concepts that were covered in class so that students can fully digest the material.
- Practice exams are similar to the exams. If you understand how to do the frqs, you can basically ace the exams.

Cons:
- Lectures are not concise. How professor lectures is that she covers concepts in an abstract way, making the homework a frustrating process as she rarely does examples. Most of how I did homework was through reading the textbook. She also assumes you remember past concepts from previous math courses.
- The grading scheme is wack. Final is worth a big chunk of your grade, at least 50% so you're stressed enough to do well on the final or you're fucked. On top of that, if you drop your midterm, she has another grading scheme with the final being worth 80%.
- Homework can take a good chunk of time. There is one homework assignment assigned each week with an average of 7-10 questions assigned. Some questions are straightforward, but others have taken me 2-3 hours to complete. Although the lowest 2 homework get dropped, expect to be on the grind each week (I'd advice to drop the homework assigned before the midterm and final).
- No cheat sheet, notes or calculators allowed for any exams (even the final)

It was an alright class. I found the material interesting. However I would have liked to see more demonstration of practical application; we mostly discussed only quadratic cost functions, but what about applications to situations where we don't know the function, like ML? Also, homework was relentless; even during midterm week we still had homework.

Meng is really an okay professor. Her lectures are very technical, which makes them a bit hard to understand, but she does try her best to go with at least some conceptual intuition. Her exams are indeed difficult, without a curve, but I wouldn’t say they are impossible to do well on. Read the textbook, understand everything from the core, and practice the medium-level homework questions (her exam questions are around that difficulty), it should be still okay. Personally, I like Meng a lot. If you ever try to approach her, she is a positive, sweet, and welcoming person. There is no pressure at all when talking with her, and she tried to help you when you need it. Overall, I would take the course again in a quarter when I have all other easy classes (if you plan to take it, consider your academic workload and the overall difficulty