MATH 156
Machine Learning
Description: Lecture, three hours; discussion, one hour. Requisites: courses 115A, 164, 170A or 170E or Statistics 100A, and Computer Science 31 or Program in Computing 10A. Strongly recommended requisite: Program in Computing 16A or Statistics 21. Introductory course on mathematical models for pattern recognition and machine learning. Topics include parametric and nonparametric probability distributions, curse of dimensionality, correlation analysis and dimensionality reduction, and concepts of decision theory. Advanced machine learning and pattern recognition problems, including data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models, and Markov random fields. Projects in MATLAB to be part of final project presented in class. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2020 - Jamie is undoubtedly a nice person who cares about her students, but this course can be unclear at times. It might be due to the unorganized nature of the textbook, I was always left in confusion throughout the quarter. However, the exams don’t even require you to understand those materials, and a basic understanding of how the algorithms work and their theoretical base should help you ace the exam (mean of 98 in final should demonstrate this point). There is a final project which should be easy as long as you find teammates who are not free riders.
Fall 2020 - Jamie is undoubtedly a nice person who cares about her students, but this course can be unclear at times. It might be due to the unorganized nature of the textbook, I was always left in confusion throughout the quarter. However, the exams don’t even require you to understand those materials, and a basic understanding of how the algorithms work and their theoretical base should help you ace the exam (mean of 98 in final should demonstrate this point). There is a final project which should be easy as long as you find teammates who are not free riders.
Most Helpful Review
Winter 2019 - (EDIT) Argh, I was the same person who wrote this initial positive review on Jacobs' Math 164 class. This time around, I continued with his Math 182 class (Algorithms). I was disappointed to see no practice exams posted, and little review done for the very problem-solving-esque material for each exam. I felt that we were essentially shooting in the dark for the midterm and final. Personally, I thought the final was easier than the midterm, but I didn't do as well as I hoped (I could probably contest for some more points). In the end, I got a decent grade, though I did want that A. I probably convinced a lot of people to take this class, and judging from how things panned out, it's honestly hit or miss. To everyone who was expecting this to be a more chill experience, I'm so sorry. I never would have expected the vibe to change like this.
Winter 2019 - (EDIT) Argh, I was the same person who wrote this initial positive review on Jacobs' Math 164 class. This time around, I continued with his Math 182 class (Algorithms). I was disappointed to see no practice exams posted, and little review done for the very problem-solving-esque material for each exam. I felt that we were essentially shooting in the dark for the midterm and final. Personally, I thought the final was easier than the midterm, but I didn't do as well as I hoped (I could probably contest for some more points). In the end, I got a decent grade, though I did want that A. I probably convinced a lot of people to take this class, and judging from how things panned out, it's honestly hit or miss. To everyone who was expecting this to be a more chill experience, I'm so sorry. I never would have expected the vibe to change like this.
Most Helpful Review
Spring 2024 - If you are motivated by machine learning, I highly suggest taking this class with professor Kassab. I found her willingness to answer questions extremely helpful. She acknowledges the rapidly evolving nature of machine learning, as well as the textbook's dense approach, so lectures offer a chance to cover additional context, such as building the intuition behind theories, calculations omitted from the textbook, (good) hand drawn visuals, examples of modern applications, connections to recent advancements, best practices, and guiding heuristics. She carries a wealth of information and is clearly passionate about the field, so her lectures always ended up feeling "too short." I certainly feel that I got my money's worth out of this class, and I highly disagree with the previous reviewer's perspective. They are a great example of how cheating is only cheating yourself. For the intrigued, these lectures will provide a tremendous opportunity for you to build a solid foundation in machine learning.
Spring 2024 - If you are motivated by machine learning, I highly suggest taking this class with professor Kassab. I found her willingness to answer questions extremely helpful. She acknowledges the rapidly evolving nature of machine learning, as well as the textbook's dense approach, so lectures offer a chance to cover additional context, such as building the intuition behind theories, calculations omitted from the textbook, (good) hand drawn visuals, examples of modern applications, connections to recent advancements, best practices, and guiding heuristics. She carries a wealth of information and is clearly passionate about the field, so her lectures always ended up feeling "too short." I certainly feel that I got my money's worth out of this class, and I highly disagree with the previous reviewer's perspective. They are a great example of how cheating is only cheating yourself. For the intrigued, these lectures will provide a tremendous opportunity for you to build a solid foundation in machine learning.
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