Machine Learning Foundations and Practice
This course covers fundamental concepts, methods, and tools for machine learning using Python. We will emphasize a learn by doing approach with a heavy reliance upon exercises and assignments in Python and utilizing modern ML packages. Jupyter notebooks will be used as a framework for combining machine learning models with notes documenting the design and development of experiments. You will learn the basics of data representation and visualization as well as common well established practices for characterizing and classifying data. You will also learn to develop and apply modern machine learning models and most importantly, understand the process that underlies the design and conduct of effective machine learning experiments.
2025 Fall Semester Details
Instructor(s)
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Nathaniel Blanchard 4_1ab8d2-bb> |
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CSB 364 4_418880-51> |
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N/A 4_b37505-e5> |
Class Schedule
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TR 11:00a – 12:15p 4_3dbf82-79> |
Bio 136 4_130cee-fd> |
Blanchard 4_983ccf-64> |
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801 4_94fc11-da> |
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Blanchard 4_521d86-ef> |
TA Office Hours
| Xander | T/Th 3.30p – 4.30p, W 9a-11a |
| Sifat | M 11a-1p, T 8p-9p |
| Artemio | M/W/F 1p-2p |
| Jiakang | M/T/W 3p -4p |
| Moinul | W 4p-5p, Th 4p-6p |