Fee may apply
"Artificial Intelligence is the new electricity."
- Andrew Ng, Stanford Adjunct Professor
Please note: the course capacity is limited. To be considered for enrollment, join the wait list and be sure to complete your NDO application. Only applicants with completed NDO applications will be admitted should a seat become available. This course will be also available next quarter.
Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.
This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.
- Anand Avati
Linear algebra, basic probability and statistics.
We strongly recommend that you review the first problem set before enrolling. If this material looks unfamiliar or too challenging, you may find this course too difficult.
- Basics concepts of machine learning
- Generative learning algorithms
- Evaluating and debugging learning algorithms
- Bias/variance tradeoff and VC dimension
- Value and policy iteration
- Q-learning and value function approximation
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
|Dates:||June 24 - August 14, 2019|
|Days:||Mon, Wed, Fri|
|Times:||1:30 pm - 3:20 pm|
Provides Stanford University credit that may later be applied towards a graduate degree or certificate. Includes access to online course materials and videos for the duration of the academic quarter. Starting Autumn 2016 there is a $100 fee per course for courses dropped before the drop deadline. Click here for more information about our Registration Policies.
We strongly recommend that you review the first problem set before enrolling. If this material looks unfamiliar or too challenging, you may find this course too difficult.Enrollment Dates: April 14 to June 17, 2019
Computer Science Department Requirement
Students taking graduate courses in Computer Science must enroll for the maximum number of units and maintain a B or better in each course in order to continue taking courses under the Non Degree Option.
This course may not currently be available to learners in some states and territories.