Go to: course materials, projects, optional TA lecture schedule, CS6758 Discussion section
Lectures
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 25 | Introduction | Overview, prereq-prelim | none, (also see materials page for review material) |
| 2 | Jan 27 | Robot Kinematics | Forward Kinematics, Inverse Kinematics | |
| 3 | Feb 1 | Learning | Supervised Learning, Linear Regression, Gradient descent | pdf (part of notes) |
| 4 | Feb 3 | Vision | Projects. Signals, Filtering and Features | |
| 5 | Feb 8 | Learning | Statistical Modeling of Sensors | first part, second part on blackboard (no notes) |
| 6 | Feb 10 | Learning | Localization: Statistical Action and Perception, Markov Chains | |
| 7 | Feb 15 | Control | Linear Systems, PID control | |
| 8 | Feb 17 | Control, Planning | Basic followers, potential field | |
| 9 | Feb 22 | Path Planning | Roadmap planners, RRT, etc. | |
| 10 | Feb 24 | Learning | Markov Process, discrete HMM | |
| 11 | Mar 1 | Learning | HMM: Inference and Learning | (see previous), paper |
| 12 | Mar 3 | Learning | Kalman filters | |
| 13 | Mar 8 | Learning | Kalman filter contd. | (see previous) |
| 14 | Mar 10 | Learning | Reinforcement Learning: MDP, Bellman eqns, Value Iteration | |
| 15 | Mar 15 | Learning | Reinforcement Learning: Value/Policy Iteration, Continuous state / Finite Horizon | (see previous) |
| 16 | Mar 29 | Learning | Logistic Regression, maximum likelihood, testing (cross-val). | |
| 17 | Mar 31 | Learning | Kernels | none |
| 18 | Apr 5 | Learning | Large Margin classifiers: SVM. | extended notes (not everything in the notes is in the course) |
| 19 | Apr 7 | Course review / office hours | No new material. | - |
| Apr 7 | Prelim | 7:30-10pm. Open notes midterm / no electronic devices. | PHL101 | |
| 20 | April 12 | Learning | SVM with margin | (see previous) |
| 21 | Apr 14 | Learning | Clustering, PCA. | (no notes) |
| 22 | April 19 | Learning | Extended Kalman Filter. | (see previous) |
| 23 | April 21 | Learning | Particle filters. | ppt |
| 24 | April 26 | Learning | Particle filters (contd). | see previous |
| 25 | Apr 29 | POMDPs | ppt | |
| 26 | May 3 | Learning | POMDP | (previous) |
| May 17, 2-5pm | Final poster presentation / demo | - | - |
Optional TA Lectures
| ### | DATE | TOPIC | NOTES | |
|---|---|---|---|---|
| TA 1 | Jan 28 | Review Session | Statistics, Basic Linear Algebra. (By Colin Ponce.) | See materials page In Hollister 110. |
| TA 2 | Feb 4 | Special recommended session | Robot Operating System (ROS). (By Marcus Lim.) | ROS Tutorials
ROS Lecture Notes 3:30-4:30pm Upson 5130 |
| TA 3 | Feb 11 | 2-Part session | 1) OpenCV Introduction. 2) Software for AR.Drone (Quadrotor). (By Cooper Bills.) |
OpenCV AR.Drone API Slides CV Guide Pending Hello Webcam Invert Image File Codebase (r10) |
| TA 4 | Feb 18 | Kinect session | Using and installing the Kinect. Processing Kinect data. (By Akram Helou) | ROS Kinect driver Skeleton tracking RGBD SLAM PCL Kinect material 2:30-3:30 |
Some of these lecture notes have been taken from the following classes: CS223A by Oussama Khatib, CS229 by Andrew Ng.