Lectures: Mon, 10:30-12:15 in HG00.308
Practice hours: Wed 10:30-12:15 in HG00.206 (wk 36-43); HG00.029 (wk 45-51)
The planning and locations may change. Check the online timetable!
Date Lecture |
Code | Topic | Date practical | Weekly exercises | Take home exercises |
Sept 3 | Paul 1 | Non-linear systems 1: Numerical integration ODEs, Root finding, Bifurcations in 1D | Sept 5 | Assignments Paul 1 - non-linear dynamics 1
Remark: Exercise 3 is shifted to the Paul 2 practical, but try to finish 3a already this week. |
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Sept 10 | Paul 2 | Non-linear systems 2: Bifurcations in 2D | Sept 12 | Assignments 1- 2 of Paul 2 - non-linear dynamics 2 + Assignment 3 of Paul 1 - non-linear dynamics 1 |
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Sep 17 | Paul 3 | Non-linear systems 3: Bifurcations in Neuronal models | Nov 19 |
Assignments 3 of Paul 2 - non-linear dynamics 2 + |
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Sep 24 | Paul 4 | Neural coding 1: Population coding, Maximum Likelihood, MAP | Sep 26 | Assignment 2 of
Paul 3 - bifurcations in neuronal models + |
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Oct 1 | Paul 5 | Neural coding 2: Fisher information | Oct 3 | Assignments Paul 5 - Coding of information | |
Oct 8 | Paul 6 | Information theory: Mutual information, Methods | Oct 10 | ||
Oct 15 | Paul 7 | Topographic Maps: Overview visual system, Kohonen map | Oct 24 | ||
Nov 5 | Bert 1 | Poisson processes First passage time model |
Nov 7 | DA
1 Ex. 1* (here is the answer of 1.1) Handouts ch.2 Ex. 1, 2, 3a DA 5 Ex. 3* |
The drift diffusion model for reaction times driftdiffusion.pdf |
Nov 12 | Bert 2 | McCulloch-Pitts neurons, IF neuron, Perceptron Gradient descent rules, logistic regression |
Nov 14 | Handouts ch.6 Ex. 2* |
Choose one of these two exercises:
Or: |
Nov 19 | Bert 3 |
Sparse visual coding Multi-layered perceptrons Deep neural networks |
Nov 21 |
Sparse coding exercise program template Natural image |
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Nov 26 | Bert 4 | Stochastic neural networks, Markov Processes Boltzmann Machines |
Nov 28 | handouts cns chapter 4 exercises 1a |
Two exercises:
And:
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Dec 3 | Bert 5 | Introduction Reinforcement learning Article Sutton Barto 1990 DA 9.1 and 9.2 Sutton Barto 1990 and DA Ch9 sheets 1-29 |
Dec 5 | Reproduce fig. 17 in Sutton Barto 1990 DA 9.5* |
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Dec 10 | Bert 6 | Control theory and theory of reinforcement learning Article Kaelbling 1996, RL Survey RL Survey and control sheets |
Dec 12 | Construct the Bayesian solution of the two armed bandit problem using dynamic programming | |
Dec 17 | Bert 7 | Reinforcement learning Dayan and Abbott 9.3 except The Direct Actor Dayan and Abbott 9.4. Sections Policy Evaluation, Policy Improvement and Generalization use Kaelbling instead RL Survey and control sheets 50, 51 Sutton Barto 1990 and DA Ch9 sheets 19 to 35 |
Dec 19 | DA 9.6, DA 9.8 and DA 9.9* | Reproduce the reinforcement learning result as presented in fig. 3 of Foster et al.. The more complex coordination based navigation model also discussed in the paper is not required. |
Check Brightspace for changes
If time permits (probably doesn't):
Exercises will be handed in one week after the assignment has been given. Exercises that are handed in on time will contribute to a total average. This total average on the scale of 0-1 will be added to the final grade.
Click here for tips for handing in your assignments.
This part has a written exam, counting for maximal 5 points.
There is no written exam. Instead you are asked to write computer programs and reports for the computer exercises as listed in the column "Take home exercises" in the Overview of lectures and assignments. These final assignments count for a maximum of 5 points.
For each exercise, hand in the code that can be run stand-alone. In addition write a report for each exercise:Hand in the final result of your examination before end January 2019.