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.
- 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
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.