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| Campus | Haverford |
| Semester | Spring 2023 |
| Registration ID | CMSCH360B001 |
| Course Title | Machine Learning |
| Credit | 1.00 |
| Department | Computer Science |
| Instructor | Grissom,Alvin |
| Times and Days | TTh 11:30am-01:00pm
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| Room Location | SHA113 |
| Additional Course Info | Class Number: 1614 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 CMSC 325, or instructor consent; Enrollment Limit: 24; Lottery Preference(s): Seniors CS majors; Junior CS majors; CS minors; Scientific Computing concentrators; Seniors; Juniors; Sophomores Div: None; Natural Science, Quantitative, C: Physical and Natural Processes (; Hav: NA, QU, C) |
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