Lectures
The dates below are old, and the lecture details will change as class progresses.
Note: The notes posted below may not be include all the material covered in the class. Please refer to what was discussed in the actual class.
# | DATE | TOPIC | NOTES | |
---|---|---|---|---|
1 | Jan 22 | Introduction | Overview, Topics Overview. Robot Kinematics. | |
2 | Jan 24 | Supervised Learning. | Gradient descent | slides, pdf (part of notes) |
3 | Jan 29 | Supervised Learning | Linear Regression, Cross-val testing. | knn, linear regression 1, 2. (Slides from Piyush Rai.) (No slides for train/test cross validation.) |
4 | Jan 31 | Robot survival kit | Probability view. ROS+Gazebo+PR2 simulator (Sung). | slides. Also see Piazza. |
5 | Feb 5 | Supervised Learning | Logistic Regression. Newton's method. | slides, optional notes |
6 | Feb 7 | Markov Chains | Robot state transitions. | - |
7 | Feb 12 | Reinforcement Learning | Decision making, MDP, Bellman eqns, Value Iteration | |
8 | Feb 14 | Reinforcement Learning | Policy Iteration, estimating robot transitions. | (see previous) |
9 | Feb 19 | Reinforcement Learning | Fitted Value Iteration, Value Function approximation | (see previous) |
10 | Feb 21 | Path Planning. | Potential Field, RRT. | |
11 | Feb 26 | Kalman Filters | Discrete Time Linear Systems.- | |
12 | Feb 28 | Kalman Filters | observations, applications to tracking | (see previous) |
13 | Mar 5 | Kalman Filters | Extended Kalman Filters. | |
14 | Mar 7 | Supervised Learning | Robotic Perception, Basic Operations, 3D Features. | PCL slides |
15 | Mar 12 | Supervised Learning | Bag of features (shape-words), 3D algorithms, Point-cloud library PCL (Anand) | slides, Also see Piazza. |
16 | Mar 14 | Control | Linear systems controllability, PID control. | |
17 | Mar 26 | Learning | discrete HMM | |
18 | Mar 28 | Learning | HMM: Inference and Learning | (see previous), paper |
19 | April 2 | Applications | Kalman and HMM application examples | pdf, more |
- | April 4 | Sprint 2 presentations | - | - |
20 | April 9 | Learning | Particle filters. | |
21 | April 11 | Particle Filters | derivation from Bayes filters | (see previous) |
22 | April 16 | Learning | Particle Filters | (previous) |
23 | April 18 | Review | - | |
Apr 18 | Prelim | 7:30-10pm. Open notes midterm / no electronic devices. | April 18, 2013 (evening) | |
24 | April 23 | POMDPs / 5min project group presentations | ppt | |
25 | April 25 | POMDPs / 5min project group presentations | (see previous) | |
26 | April 30 | Robot Learning Applications / 5min project group presentations | . | |
27 | May 2 | Robot Learning Applications / 5min project group presentations | . | |
-- | Final poster presentation / demo | Thu, May 09, 2013 02:00 PM - 04:30 PM | ||
-- | Final written reports due | May 15 midnight 2013 (absolutely NO extensions) |
Some of these lecture notes have been taken from the following classes: CS223A by Oussama Khatib, CS229 by Andrew Ng.