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Go to: course materials, projects, optional TA lecture schedule, CS6758 Discussion section


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.

1 Jan 25 Introduction Overview, prereq-prelim none, (also see materials page for review material)
2 Jan 27 Robot Kinematics Forward Kinematics, Inverse Kinematics pdf
3 Feb 1 Learning Supervised Learning, Linear Regression, Gradient descent pdf (part of notes)
4 Feb 3 Vision Projects. Signals, Filtering and Features pdf
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 pdf
7 Feb 15 Control Linear Systems, PID control pdf
8 Feb 17 Control, Planning Basic followers, potential field pdf
9 Feb 22 Path Planning Roadmap planners, RRT, etc. pdf
10 Feb 24 Learning Markov Process, discrete HMM pdf
11 Mar 1 Learning HMM: Inference and Learning (see previous), paper
12 Mar 3 Learning Kalman filters pdf
13 Mar 8 Learning Kalman filter contd. (see previous)
14 Mar 10 Learning Reinforcement Learning: MDP, Bellman eqns, Value Iteration pdf
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). pdf
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

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.)
AR.Drone API
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
Kinect material

Some of these lecture notes have been taken from the following classes: CS223A by Oussama Khatib, CS229 by Andrew Ng.