Difference between revisions of "CS 4641"
(Created page with "{{DISPLAYTITLE|CS 4641 - Machine Learning}} '''CS 4641: Machine Learning''' is a 3-credit Computer Science course about machine learning. The graduate level of the course...") |
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Machine learning is the task of having computer learn from data to make predictions, insights, or decisions. |
Machine learning is the task of having computer learn from data to make predictions, insights, or decisions. |
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| − | Coursework includes math/programming assignments, tests/quizzes, and towards the end of the semester, a team project using machine learning. |
+ | Coursework includes math/programming assignments, tests/quizzes, and towards the end of the semester, a team project using machine learning for a topic of your choice. |
Course topics include:<ref>https://nakulgopalan.github.io/cs4641/</ref> |
Course topics include:<ref>https://nakulgopalan.github.io/cs4641/</ref> |
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* [https://nakulgopalan.github.io/cs4641/ Course syllabus, schedule, and slides, Spring 2021] |
* [https://nakulgopalan.github.io/cs4641/ Course syllabus, schedule, and slides, Spring 2021] |
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| + | * [https://www.edx.org/course/machine-learning-4 Free online course videos on edX by Charles Isbell] |
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== References == |
== References == |
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Revision as of 12:48, 30 June 2021
CS 4641: Machine Learning is a 3-credit Computer Science course about machine learning. The graduate level of the course is CS 7641, which has additional homework problems.
Overview
Machine learning is the task of having computer learn from data to make predictions, insights, or decisions.
Coursework includes math/programming assignments, tests/quizzes, and towards the end of the semester, a team project using machine learning for a topic of your choice.
Course topics include:[1]
- Review of math fundamentals
- Linear algebra
- Probability and statistics
- Information theory
- Optimization
- Supervised learning: making predictions from data
- Tree-based models
- Support vector machines
- Linear classification and regression
- Neural networks
- Unsupervised learning: drawing insights from unlabeled data
- Clustering
- Dimensionality reduction
- Kernel density estimation
- Hidden Markov models
- Reinforcement learning
The course does not closely follow any textbook, but the slides are reasonably detailed.
The only prerequisite is CS 1331, but really you should have a good grounding in probability and statistics, linear algebra, and a bit of multivariable calculus, and have basic programming experience in Python.
The median student in CS 4641 spent 11 hours per week on the course in Spring 2021, according to SmartEvals. For CS 7641, the median student spent 11–19 hours per week, depending on the section and semester.
Resources
- Course syllabus, schedule, and slides, Spring 2021
- Free online course videos on edX by Charles Isbell
