"Artificial Intelligence is the new electricity."
- Andrew Ng, Stanford Adjunct Professor
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.
Please note that in past years there has been high demand for places in this course. Once enrollment opens, applying as soon as possible is strongly recommended.
- Andrew Ng Adjunct Professor, Computer Science
- 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.
3.0 - 4.0
Students enrolling under the non degree option are required to take the course for 4.0 units.
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.