COM SCI 260
Machine Learning Algorithms
Description: Lecture, four hours; discussion, two hours; outside study, six hours. Recommended requisite: course 180. Problems of identifying patterns in data. Machine learning allows computers to learn potentially complex patterns from data and to make decisions based on these patterns. Introduction to fundamentals of this discipline to provide both conceptual grounding and practical experience with several learning algorithms. Techniques and examples used in areas such as healthcare, financial systems, commerce, and social networking. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2022 - Lectures: He is not the best lecturer. For the more mathematical/theoretical content, I learned more from the book. For the content on neural networks, he didn’t even seem familiar with the slides. But the networks content also is only on quizzes, so I guess it’s ok. Homework 35%: They are very mathematical questions until the switch into talking about networks, then the homework becomes implementing networks by scratch in Python. It was also a grind for the math homeworks because they were required to be in LaTeX. Midterm 30%: They generously gave a very similar practice midterm, so as long as you can confidently solve those problems, or at least note how to solve them on your cheatsheet, you’ll score well. It also covers the math content more. No final, 6 quizzes that are only worth 5% total of grade. Final project 30%: You can choose from some given proposals that Prof. asked people he knows to provide some ideas for, or pursue your own project if you’re smart. Would recommend trying to take the course with friends/colleagues you trust, so that you can collaborate with no issues. Overall, I would recommend this class if you are mathematically mature, have an interest in the math in ML theory, or are just very ML-invested. Or have an interest in self-learning all the math and network concepts. I unfortunately have little qualification in any of the above, so I suffered silently. Sunk cost fallacy is real. Shoutout to Lucas Tecot for the HW hints.
Fall 2022 - Lectures: He is not the best lecturer. For the more mathematical/theoretical content, I learned more from the book. For the content on neural networks, he didn’t even seem familiar with the slides. But the networks content also is only on quizzes, so I guess it’s ok. Homework 35%: They are very mathematical questions until the switch into talking about networks, then the homework becomes implementing networks by scratch in Python. It was also a grind for the math homeworks because they were required to be in LaTeX. Midterm 30%: They generously gave a very similar practice midterm, so as long as you can confidently solve those problems, or at least note how to solve them on your cheatsheet, you’ll score well. It also covers the math content more. No final, 6 quizzes that are only worth 5% total of grade. Final project 30%: You can choose from some given proposals that Prof. asked people he knows to provide some ideas for, or pursue your own project if you’re smart. Would recommend trying to take the course with friends/colleagues you trust, so that you can collaborate with no issues. Overall, I would recommend this class if you are mathematically mature, have an interest in the math in ML theory, or are just very ML-invested. Or have an interest in self-learning all the math and network concepts. I unfortunately have little qualification in any of the above, so I suffered silently. Sunk cost fallacy is real. Shoutout to Lucas Tecot for the HW hints.
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