EC ENGR M146
Introduction to Machine Learning
Description: (Same as Computer Science M146.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 131A or Civil and Environmental Engineering 110 or Mathematics 170A or 170E or Statistics 100A; Computer Science 32 or Program in Computing 10C; Mathematics 33A. Introduction to breadth of data science. Foundations for modeling data sources, principles of operation of common tools for data analysis, and application of tools and models to data gathering and analysis. Topics include statistical foundations, regression, classification, kernel methods, clustering, expectation maximization, principal component analysis, decision theory, reinforcement learning and deep learning. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Spring 2024 - Professor Diggavi is definitely an expert in the field of ML, as he would often share with us stories behind the development of ML algorithms. Even though his lectures are dry and math-heavy, he is able to explain most of the abstract concepts clearly. Contrary to popular opinion, I actually appreciated the time and effort he put into going through the mathematical derivations behind the theorems and algorithms. Exams were on the tough end but fair - just make sure to include all the key concepts + proofs in your cheatsheet, and fully understand the practice exam. However, homework specs can be confusing at times, with a couple of mistakes here and there. Fortunately, the TAs (esp Sadik) were really responsive on Campuswire to clarify any doubts we had. Overall, I do think this is a well-structured course, especially if you are keen to learn more about the math behind ML, which complements well with the more applied ML courses like ECE C147 and CS 162/163. I do think that taking 115A and 170S concurrently with this class helped me a lot. As mentioned in the previous comments, classes like PIC 16A, Math 115A, Math 170S and CS M148 are helpful pre-requisites.
Spring 2024 - Professor Diggavi is definitely an expert in the field of ML, as he would often share with us stories behind the development of ML algorithms. Even though his lectures are dry and math-heavy, he is able to explain most of the abstract concepts clearly. Contrary to popular opinion, I actually appreciated the time and effort he put into going through the mathematical derivations behind the theorems and algorithms. Exams were on the tough end but fair - just make sure to include all the key concepts + proofs in your cheatsheet, and fully understand the practice exam. However, homework specs can be confusing at times, with a couple of mistakes here and there. Fortunately, the TAs (esp Sadik) were really responsive on Campuswire to clarify any doubts we had. Overall, I do think this is a well-structured course, especially if you are keen to learn more about the math behind ML, which complements well with the more applied ML courses like ECE C147 and CS 162/163. I do think that taking 115A and 170S concurrently with this class helped me a lot. As mentioned in the previous comments, classes like PIC 16A, Math 115A, Math 170S and CS M148 are helpful pre-requisites.
Most Helpful Review
Spring 2019 - This was the first time Professor Doleček taught Machine Learning. Having taken Electrical and Computer Engineering 131A (Probability) with Professor Doleček, this class was a minor disappointment. Especially near the beginning of the class, the lectures were fairly unclear – to this day, I don't have the strongest grasp of Bayesian statistical terms (prior, posterior, likelihood) that the student is expected to know for the rest of the class. However, her teaching settled down a bit after a few weeks, but it somehow never quite seemed to reach the clarity of her 131A lectures. Compared to 131A, this class was around the same difficulty level. The homework had a lot of strenuous calculus in it, but you do learn a lot if you were to put in the effort to do them. (Apparently the TAs explain them in some level of detail, but I found it difficult to understand them so chose not to go to discussions most of the time. They did post notes though, which I didn't find out till week 7 or so. Oops.) On the other hand, the exams were a few orders of magnitude easier. Perhaps it's just because it was the first time Professor Doleček taught this class, but the exams were pretty much the same things as homework problems, with some conceptual questions mixed in. Also check out my review for course 131A: https://bruinwalk.com/professors/lara-dolecek/ec-engr-131a/, and search for “one of the hardest classes.”
Spring 2019 - This was the first time Professor Doleček taught Machine Learning. Having taken Electrical and Computer Engineering 131A (Probability) with Professor Doleček, this class was a minor disappointment. Especially near the beginning of the class, the lectures were fairly unclear – to this day, I don't have the strongest grasp of Bayesian statistical terms (prior, posterior, likelihood) that the student is expected to know for the rest of the class. However, her teaching settled down a bit after a few weeks, but it somehow never quite seemed to reach the clarity of her 131A lectures. Compared to 131A, this class was around the same difficulty level. The homework had a lot of strenuous calculus in it, but you do learn a lot if you were to put in the effort to do them. (Apparently the TAs explain them in some level of detail, but I found it difficult to understand them so chose not to go to discussions most of the time. They did post notes though, which I didn't find out till week 7 or so. Oops.) On the other hand, the exams were a few orders of magnitude easier. Perhaps it's just because it was the first time Professor Doleček taught this class, but the exams were pretty much the same things as homework problems, with some conceptual questions mixed in. Also check out my review for course 131A: https://bruinwalk.com/professors/lara-dolecek/ec-engr-131a/, and search for “one of the hardest classes.”
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