This course is an introduction to the foundations behind modern data analysis and machine learning. The first part of the course covers selected topics from probability theory and linear algebra that are key components of modern data analysis. Next, we cover multivariate statistical techniques for dimensionality reduction, regression, and classification. Finally, we survey recent topics in machine learning, in particular, deep neural networks.
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Time: Tue/Thu 11:00 AM - 12:15 PM
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Location: Thornton E303 and online via Zoom
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Instructor: Tom Fletcher (ptf8v AT virginia DOT edu)
- Office Hours: Wednesdays 3 - 4 PM
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TA: Yinzhu Jin
- Office Hours: Thursdays 3 - 4 PM
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TA: Ruixuan Tang
- Office Hours: Tuesdays 3 - 4 PM
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TA: Xuwang Yin
- Office Hours: Mondays 10 - 11 AM
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TA: Guangtao Zheng
- Office Hours: Mondays 2 - 3 PM
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Textbook: Some readings from Mathematical Foundations for Data Analysis, by Jeff Phillips
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Prerequisites: You should be comfortable programming in Python (CS 2110 or equivalent is sufficient)
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Software: All homeworks will be done in Jupyter
This class was inspired by Jeff Phillips' University of Utah course, CS 4964: Foundations of Data Analysis