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 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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