MATH 164
Optimization
Description: Lecture, three hours; discussion, one hour. Enforced requisites: courses 115A, 131A. Not open for credit to students with credit for former Electrical Engineering 136. Fundamentals of optimization. Linear programming: basic solutions, simplex method, duality theory. Unconstrained optimization, Newton method for minimization. Nonlinear programming, optimality conditions for constrained problems. Additional topics from linear and nonlinear programming. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Spring 2024 - 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)
Spring 2024 - 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)
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