CS229 is the undergraduate machine learning course at Stanford. You can watch the lectures on iTunesU and Youtube. We are going to be working through the course at one lecture a week starting 1 September 2010 and finishing in January 2011. There are four problem sets which we'll be doing one every 5 weeks.
The plan is to watch the lectures in your own time. We'll be discussing our solutions to problem sets every 5 weeks. Bring any questions about the course you have along to a meeting and there might be someone there who can help you out.
- there is no instructor at Noisebridge - this is just a study group.
- We are taking the course at a slower rate than the actual course (which is currently in session at the farm).
- Not everyone is at the same point in the course - its ok if you want to start today, there are others who have recently started too.
 Course Description
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.
- one lecture a week
- one problem set every five weeks
 Supplemental Materials
 Problem Sets from 2009
- Problem set 1: File:CS229 ps1.pdf
 Progress: Watching Lectures
|Name||Lecture 1||Lecture 2||Lecture 3||Lecture 4|| Lecture 5
|Lecture 6||Lecture 7||Lecture 8||Lecture 9|| Lecture 10
|Lecture 11||Lecture 12||Lecture 13||Lecture 14|| Lecture 15
|Lecture 16||Lecture 17||Lecture 18||Lecture 19|| Lecture 20|
 Progress: Assignments
|Name|| Problem set 1
| Problem set 2
| Problem set 3
| Problem set 4|