Here’s your roadmap for the semester!
- Readings should be completed before each class session
- Assignments are due by 11:59 PM on the day they are due
- Class materials (slides, in-class activities, etc.) will be added on the day of class
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| September 1 |
Learning in Humans and Machines |
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| September 3 |
R, RStudio, and Github |
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| September 8 |
Associative Learning |
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| September 10 |
How far can simple associative learning get you? |
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| September 15 |
Perceptrons |
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| September 16 |
Implementing the Rescorla-Wagner Model |
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| September 17 |
Multi-layer Networks |
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| September 22 |
Backpropagation details |
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| September 24 |
Limits to connectionism |
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| September 29 |
Recurrent neural networks |
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| October 9 |
Perceptrons and backpropagation |
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| October 1 |
Basics of Bayesian Inference |
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| October 6 |
Learning by Bayesian inference |
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| October 8 |
Models at different levels |
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| October 13 |
Rational analysis |
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| October 15 |
Inference by sampling |
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| October 20 |
Bayesian associative learning |
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| October 21 |
The number game |
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| October 22 |
Comparing models |
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| October 27 |
Machines that learn like people |
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| November 6 |
Markov chain Monte Carlo |
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| October 29 |
Learning from teaching |
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| Nov 3 |
What makes a good teacher? |
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| Nov 5 |
Rational speech acts |
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| Nov 6 |
Project Proposal |
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| Nov 10 |
Indirectly learning from language |
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| Nov 12 |
Iterated learning |
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| Nov 17 |
Community effects on learning from others |
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| Nov 19 |
The structure in language |
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| Nov 24 |
Modern language models |
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| Nov 26 |
Thanksgiving - No class |
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| Dec 1 |
Why training data matter |
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| Dec 3 |
Project Presentations |
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| Dec 8 |
Project Presentations |
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| Dec 10 |
Wrap up |
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| December 15 |
Final Project due |
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