STANFORD

 CS229A
 Applied Machine Learning
 Autumn 2011


Announcements

About the class


What is machine learning?
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

What is CS229A?
This class' emphasis is on Applied Machine Learning. Concretely, we want to give you the practical skills needed to get learning algorithms to work. Compared to CS229 (Machine Learning), we cover fewer learning algorithms, and also spend less time on the math and theory of machine learning, but spend much more time on the pratical, hands-on skills (and "dirty tricks") for getting this stuff to work well on an application. More of the homeworks will also focus on giving you practice implementing, modifying and debugging learning algorithms, and less on the mathematical underpinnings of machine learning.

How will this class work?
This is an online and largely self-paced class, and the majority of the class content will be delivered via online videos. Even though there is a regular class meeting time where we will discuss machine learning and extensions to the course material, most of the in-person Monday meetings are optional.

How can I find out more about the class?
Additional information is on the Course Information page. Common questions are also answered on the FAQ page.

How do I sign up?
Please sign up on Axess, and come to the first meeting on September 26th. Because this is the first time we're offering this class, enrollment is limited (tentatively to about 30 students). If the class is oversubscribed, we may give priority to students who have not taken and are not currently taking CS229.

Course Information

Course Instructor:

Andrew Ng.

Email: cs229a-qa@cs.stanford.edu

Class meetings:

3-4 units. Hewlett 103. Mondays 4.15-5.30pm.

More information: