| Date | Topic | Assignments |
| Aug. 24 | Introduction | send email to sschaal@usc.edu with two lines:
# First_Name Last_Name
your_email@mailer.com |
| Aug. 31 | Efficient learning with linear models: Locally Weighted Learning (LWL) | Incremental LWL,
LWL for Control,
Slides |
| Sept. 7 | Locally Weighted Learning (LWL) and Dimensionality Reduction | Dimensionality Reduction, Slides |
| Sept. 14 | Reinforcement Learning with Policy Gradients | Reinforcement Learning,
Policy Gradients, Slides |
| Sept. 21 | Robot Learning Talk by Martin Riedmiller,
Finish Policy Gradients, | Reinforcement Learning,
Policy Gradients, Slides |
| Sept. 28 | Bayesian Learning with EM and Variational Approximations,
Gaussian Processes | Introduction, ConvexDuality, MacKay Ch.33, Gaussian Processes |
| Oct. 5 | Learning/Imitation with Motor Primitives | Learning Motor Primitives,
Learning Attractor Landscapes as Movement Primitives,
see also Imitation Learning and Motor Primitives |
| Oct. 12 | Project Suggestions | PPT Slides |
| Oct. 19 | Project Discussion | Groups introduce their projects, goals, approaches |
| Oct. 26 | Project Progress, Discussions,
Maximum Margin Planning | Groups present their project progress,
Paper 1,Paper 2 |
| Nov. 2 | Personal meetings with groups | |
| Nov. 9 | Groups present their project progress,
Finish Maximum Margin Planning discussion,
Product of Experts |
Paper 1 |
| Nov. 16 | Product of Experts,
Remarks on on-line learning,
Personal meetings with groups on demand. | |
| Nov. 30 | Final Project presentations of all groups. | |
