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| Campus | Haverford |
| Semester | Spring 2025 |
| Registration ID | CMSCH360B001 |
| Course Title | Machine Learning |
| Credit | 1.00 |
| Department | Computer Science |
| Instructor | Grissom,Alvin |
| Times and Days | TTh 02:30pm-03:55pm
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| Room Location | |
| Additional Course Info | Class Number: 1347 To explore both classical and modern approaches, with an emphasis on theoretical understanding. There will be a significant math component (statistics and probability in particular), as well as a substantial implementation component (as opposed to using high-level libraries). However, during the last part of the course we will use a few modern libraries such as TensorFlow and Keras. By the end of this course, students should be able to form a hypothesis about a dataset of interest, use a variety of methods and approaches to test your hypothesis, and be able to interpret the results to form a meaningful conclusion. We will focus on real-world, publicly available datasets, not generating new data.; Prerequisite(s): CMSC 260 or instructor consent; Enrollment Limit: 24; Lottery Preference: 1. senior CMSC majors; 2. junior CMSC majors; 3. senior CMSC minors; 4. junior CMSC minors; 5. Scientific Computing concentrators; 6. senior LING majors; 7. junior LING majors; 8. other seniors; 9. other juniors; 10. sophomores; 11. everyone else Natural Science, Quantitative, C: Physical and Natural Processes (; Hav: NA, QU, C) |
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