Difference between revisions of "CS 4641"
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The course does not closely follow any textbook, but the slides are reasonably detailed. |
The course does not closely follow any textbook, but the slides are reasonably detailed. |
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| − | 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 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, before taking this course. The reinforcement learning unit has some overlap with [[CS 3600|CS 3600: Introduction to Artificial Intelligence]], so it may be helpful to take CS 3600 beforehand or at the same time. |
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. |
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. |
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Revision as of 13:57, 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, before taking this course. The reinforcement learning unit has some overlap with CS 3600: Introduction to Artificial Intelligence, so it may be helpful to take CS 3600 beforehand or at the same time.
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
