Course Coordinator: 
Kenji Doya
Adaptive Systems

This course is based on a book KD is writing, "Brain Computation: A Hands-on Guidebook" using Jupyter notebook with Python codes.
The course will be in a "flipped learning" style; each week, students read a draft chapter and experiment with sample codes before the class.
In the first class of the week, they present what they have learned and raise questions.
In the second class of the week, they 1) present a paper in the reference list, 2) solve exercise problem(s), 3) make a new exercise problem and solve it, or 4) propose revisions in the chapter.
Toward the end of the course, students work on individual or group projects by picking any of the methods introduced in the course and apply that to a problem of their interest.
Students are assumed to be familiar with Python, as covered in the Computational Methods course in Term 1, and basic statistics, as coverd in the Statistical Tests and Statistical Modeling courses in Term 2.

This course aims to provide common mathematical frameworks for adaptation at different scales and to link them with biological reality.
Course Content: 

1. Introduction
2. Computing Neurons
3. Supervised Learning
4. Reinforcement Learning
5. Unsupervised Learning
6. Bayesian Approaches
7. Deep Learning
8. Multiple Agents
9. Learning to Learn
10. Project Presentation

Course Type: 
Midterm Reports 60% (2 x 30%), Final Exam 40%.
Text Book: 
Rich Sutton & Andy Barto (2018) Reinforcement Learning: An Introduction, 2nd edition. MIT Press (http://incompleteideas.net/book/the-book-2nd.html)
Ian Goodfellow, Yoshua Bengio & Aaron Courville (2016) Deep Learning. MIT Press (http://www.deeplearningbook.org)
Reference Book: 
Kenji Doya (in preparation) Brain Computation: A Hands-on Guidebook. (https://github.com/oist/BrainComputation)
Chris Bishop (2006) Pattern Recognition and Machine Learning. Springer
Rich Sutton & Andy Barto (2018) Reinforcement Learning: An Introduction, 2nd edition. MIT Press (http://incompleteideas.net/book/the-book-2nd.html)
Jörgen Weibull (1995) Evolutionary Game Theory. MIT Press.
Python Scientific Lecture Notes (http://www.scipy-lectures.org)
Prior Knowledge: 

Assumes good knowledge of statistics and ability to look at biological problems in a mathematical way.

OIST courses to complete beforehand:  B31Statistical Tests and/or B32 Statistical Modeling