Week 1 (August 23): Course Introduction, Bayesian Linear Regression |
Wednesday: Course Introduction, Bayesian Linear Regression
— Assignment 1 Posted
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Required Reading: (Rasmussen & Williams) Ch. 1, 2.1
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Week 2 (August 30): Gaussian Processes |
Monday: Gaussian Process Regression
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Required Reading: (Rasmussen & Williams) Ch. 2.2-2.6
Recommended Reading: Exploring Gaussian Processes
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Wednesday: Gaussian Process Classification, pt. 1
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Required Reading: (Rasmussen & Williams) Ch. 3.1-3.4
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Friday:
— Assignment 1 Due
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Week 3 (September 6): Classification, Model Selection |
Monday: Gaussian Process Classification, pt. 2, Model Selection Occam’s Razor notebook
— Assignment 2 Posted
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Required Reading: (Rasmussen & Williams) Ch. 3.5, 5.1-5.3
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Wednesday: Model Selection keynote, pdf
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Required Reading: (Rasmussen & Williams) Ch. 5.3-5.5
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Week 4 (September 13): Approximation Methods |
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Week 5 (September 20): Covariance functions, Learning Theory |
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Week 6 (September 27): Latent Variable Models, Neural Networks |
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Week 7 (October 4): Other Models, Midterm |
Monday: Connections to other models, Midterm Review keynote, pdf
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Required Reading: (Rasmussen & Williams) Ch. 6
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Wednesday: Midterm
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Week 8 (October 11): Project Proposals, Multi-Task Learning |
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Week 9 (October 18): Multi-Task Learning, Domain Adaptation |
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Week 10 (October 25): Domain Adaptation |
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Week 11 (November 1): Online Learning, Active Learning |
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Week 12 (November 8): Active Learning |
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Week 13 (November 15): Zero-shot Learning |
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Week 14 (November 22): Thanksgiving Break - no classes |
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Week 15 (November 29): Few-shot Learning, Meta Learning |
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Week 16 (December 6): Project Presentations |
Monday: Project Presentations
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