Arvutiteaduse instituut
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  2. 2018/19 sügis
  3. Erikursus masinõppes: Stiimulõpe närvivõrkudega (MTAT.03.317)
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Erikursus masinõppes: Stiimulõpe närvivõrkudega 2018/19 sügis

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Timetable

Fall 2018

2018-09-06Lecture 1: Introduction and Course Overview (slides) (test)(watch in the class together)
2018-09-13Lecture 2: Supervised Learning and Imitation (slides) (test)(watch home, test and discussion in class)
2018-09-20Lecture 3: TensorFlow and Neural Nets Review Session
(slides) (notebook)
(watch home if needed, no test)
 Homework 1: Imitation Learning (code)(solution presentations, deadline next day)
2018-09-27Lecture 4: Reinforcement Learning Introduction (slides) (test)(watch home, test and discussion in class)
2018-10-04Lecture 5: Policy Gradients Introduction (slides) (test)(watch home, test and discussion in class)
2018-10-11Homework 2: Policy Gradients (code)(solution presentations 1-5, deadline next day)
2018-10-18Lecture 6: Actor-Critic Introduction (slides) (test)(watch home, test and discussion in class)
2018-10-25Homework 2: Policy Gradients (code)(solution presentations 6-8, deadline next day)
2018-11-01Lecture 7: Value Functions and Q-Learning (slides) (test)(watch home, test and discussion in class)
2018-11-08Lecture 8: Advanced Q-Learning Algorithms (slides) (test)(watch home, test and discussion in class)
2018-11-15Homework 3: Q-Learning and Actor-Critic (code)(solution presentations, deadline next day)
2018-11-22Lecture 9: Advanced Policy Gradients (slides) (test)(watch home, test and discussion in class)
2018-11-29Lecture 10: Optimal Control and Planning (slides) (test)(watch home, test and discussion in class)
2018-12-06Lecture 11: Model-Based Reinforcement Learning (slides) (test)(watch home, test and discussion in class)
2018-12-13Homework 4: Model-Based RL (code)(solution presentations, deadline next day)
2018-12-20Lecture 12: Advanced Model Learning and Images (slides)(watch home, test and discussion in class)
???Lecture 13: Learning Policies by Imitating Other Policies (slides)(watch home, test and discussion in class)

Spring 2019

2019-02-14Lecture 14: Probability and Variational Inference Primer(watch home, test and discussion in class)
2019-02-21Lecture 15: Connection between Inference and Control(watch home, test and discussion in class)
2019-02-28Lecture 16: Inverse Reinforcement Learning(watch home, test and discussion in class)
2019-03-07Homework 5: Advanced Topics(solution presentations, deadline next day)
2019-03-14Lecture 17: Exploration: Part 1(watch home, test and discussion in class)
2019-03-21Lecture 18: Exploration: Part 2(watch home, test and discussion in class)
2019-03-28Lecture 19: Transfer Learning and Multi-Task Learning(watch home, test and discussion in class)
2019-04-04Project milestone 1(presentations from teams?)
2019-04-11Lecture 20: Meta-Learning(watch home, test and discussion in class)
2019-04-18Lecture 21: Parallelism and RL System Design(watch home, test and discussion in class)
2019-04-25Lecture 22: Advanced Imitation Learning and Open Problems(watch home, test and discussion in class)
2019-05-02Project milestone 2(presentations from teams?)
2019-05-09Lecture 23: Guest Lecture: Craig Boutilier(watch home, no test)
 Lecture 24: Guest Lecture: Kate Rakelly & Gregory Kahn(watch home, no test)
2019-05-16Lecture 25: Guest Lecture: Quoc Le(watch home, no test)
 Lecture 26: Guest Lecture: Karol Hausman(watch home, no test)
2019-05-23Project presentations 1
2019-05-30Project presentations 2
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