Search

Learning in Humans and Machines

Learning in Humans and Machines

Syllabus
Schedule
Readings
Assignments
Classes
Piazza

1: Learning in humans and machines
2: R, RStudio, and Github
3: Associative learning
4: How far can simple associative learning get you?
5: Perceptrons
6: Multi-layer networks
7: Backpropagation details
8: Limits to connectionism
9: Recurrent neural networks
10: Bayesian inference
10: Learning by Bayesian inference
12: Models at different levels
13: Rational Analysis
14: Inference by sampling
15: Bayesian associative learning
16: Comparing models
17: Machines that learn like people
18: Learning from teaching
19: What make sa good teacher?
20: Rational speech acts
21: Learning indirectly from language
22: Iterated learning
23: Community effects on learning from others
24: The structure in language
25: Modern language models
26: Why training data matter
27: Project presentations
28: Project presentations

Project presentations

Materials from class on Tuesday, December 8, 2020

Recording

Recording

Lecture recording on CMU box.

Last updated on December 8, 2020

85426/85726: Learning in Humans and Machines (Fall 2020)
Carnegie Mellon University Department of Psychology

Dr. Dan Yurovsky yurovsky@cmu.edu

Tuesday/Thursday 1:30–2:50 PM
Zoom Room

All content licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Content 2020 Dan Yurovsky

This site created with the Academic theme in blogdown and Hugo.

View the source at GitHub.

Cite