Below is a tentative schedule of lectures and
labs. Schedule and topics are subject to change (probably not
much)
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lecture
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lab
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week 1 (08/28,30)
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course overview, what is machine learning
reading assignment: chapter 1, sections 2.1 - 2.7
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intro to Python's environment, intro to
LaTeX
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week 2 (09/04,06)
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sections 2.1 - 2.7
reading assignment: sections 2.8 - 2.12
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Python tutorial, Python notebooks
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week 3 (09/11,13)
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sections 2.7 - 2.12
reading assignment: sections 3.1 - 3.8
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Linear Algebra in Python
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week 4 (09/18,20)
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sections 2.12, 3.1 - 3.8
reading assignment: sections 3.9 - 3.14
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PCA application: image compression
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week 5 (09/25,27)
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sections 3.8 - 3.14
reading assignment: sections 4.1 - 4.3
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Python Tools for Data I
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week 6 (10/02,10/04)
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sections 4.1 - 4.3
reading assignment: sections 4.4 - 4.5
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Data tools: PCA and Data Visualization
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week 7 (10/09,11)
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sections 4.3 - 4.5
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Least Squares
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week 8 (10/16,18)
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midterm
review
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midterm
exam
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week 9 (10/23,25)
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section 5.1
reading assignment: section 5.2
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Optimization, Gradient Descent
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week 10 (10/30,11/01)
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section 5.2
reading assignment: sections 5.3 - 5.4
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Linear Regression
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week 11 (11/06,08)
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sections 5.3 - 5.4
reading assignment: section 5.5
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Supervised Classification
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week 12 (11/13,15)
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sections 5.4 - 5.5
reading assignment: sections 5.6 - 5.7
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Bias vs. Variance
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week 13 (11/20,22)
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sections 5.5 - 5.7
reading assignment: sections 5.8 - 5.9
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No Lab, Thanksgiving
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week 14 (11/27,29)
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sections 5.7 - 5.9
reading assignment: sections 5.10 - 5.11
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Stochastic Gradient Descent
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week 15 (12/04,06)
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sections 5.10 - 5.11
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Neural Networks
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week 16 (12/11,13)
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final review
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no lab, study day
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week 17 (12/18,20)
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final exam
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no lab
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back to math
382
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