COM SCI M146
Introduction to Machine Learning
Description: (Same as Electrical and Computer Engineering M146.) Lecture, four hours; discussion, one hour; outside study, seven hours. Requisites: course 33, and Civil and Environmental Engineering 110 or Electrical and Computer Engineering 131A or Mathematics 170A or 170E or Statistics 100A. 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
Fall 2019 - This class was hard. It was very math-heavy and filled with proofs. If you are not super familiar with the material like I was, prepare to spend a lot of time trying to understand what is going on outside of class. Kai-Wei didn't do a great job explaining any of the concepts that we went over and could be quite confusing when explaining the math behind different models. There are only four problem sets in the course, but they can take a bit of time to do, so the workload isn't too bad for the most part. The exam questions are generally a little bit easier than the homework questions, but there is a lot of probability related questions on exams. The curve for the class this quarter was as follows (He won't curve down only up): A+ 11, A 33, A- 33, B+ 35, B 22, B- 23, C+ 14, C 14, Other 6 (# of people per grade not %). Although the material was pretty challenging and Kai-Wei didn't do an excellent job explaining it, I found the class to be semi-interesting. I thought it was cool to understand what machine learning really is and what goes into making a model. PAC Theory was especially interesting in my opinion!
Fall 2019 - This class was hard. It was very math-heavy and filled with proofs. If you are not super familiar with the material like I was, prepare to spend a lot of time trying to understand what is going on outside of class. Kai-Wei didn't do a great job explaining any of the concepts that we went over and could be quite confusing when explaining the math behind different models. There are only four problem sets in the course, but they can take a bit of time to do, so the workload isn't too bad for the most part. The exam questions are generally a little bit easier than the homework questions, but there is a lot of probability related questions on exams. The curve for the class this quarter was as follows (He won't curve down only up): A+ 11, A 33, A- 33, B+ 35, B 22, B- 23, C+ 14, C 14, Other 6 (# of people per grade not %). Although the material was pretty challenging and Kai-Wei didn't do an excellent job explaining it, I found the class to be semi-interesting. I thought it was cool to understand what machine learning really is and what goes into making a model. PAC Theory was especially interesting in my opinion!
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Most Helpful Review
Spring 2020 - Prof. Dolecek is a good person, and she is very knowledgeable when it comes to the course material. I want to make this clear that she is NOT a bad person or mean or anything. That being said, there are some points that you should know if you were to choose her lecture (especially for remote learning): 1. She didn't use zoom. All lectures are pre-recorded and posted on CCLE for my quarter. 2. She has terrible, terrible handwriting. Sometimes you cannot tell subtractions apart from multiplications (she writes ยท and - really casually), also from time to time her writing becomes unreadable and you have to rely fully on listening. 3. For some reason, in the middle of the quarter she switched from ball-point to highlighter to write on her slides, just when you think her handwriting cannot get any worse... So pretty much her handwriting has made this course harder than it should be, and the highlighter is plain suffer for remote learning. But again, Prof. Dolecek is a good person, she would answer questions and can explain stuff for you when you are stuck.
Spring 2020 - Prof. Dolecek is a good person, and she is very knowledgeable when it comes to the course material. I want to make this clear that she is NOT a bad person or mean or anything. That being said, there are some points that you should know if you were to choose her lecture (especially for remote learning): 1. She didn't use zoom. All lectures are pre-recorded and posted on CCLE for my quarter. 2. She has terrible, terrible handwriting. Sometimes you cannot tell subtractions apart from multiplications (she writes ยท and - really casually), also from time to time her writing becomes unreadable and you have to rely fully on listening. 3. For some reason, in the middle of the quarter she switched from ball-point to highlighter to write on her slides, just when you think her handwriting cannot get any worse... So pretty much her handwriting has made this course harder than it should be, and the highlighter is plain suffer for remote learning. But again, Prof. Dolecek is a good person, she would answer questions and can explain stuff for you when you are stuck.
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