CSE 151BDeep Learning
This course covers the fundamentals of deep neural networks at the undergraduate level. We introduce linear regression, multi-layer perceptrons, backpropagation, and automatic differentiation. We will also discuss convolutional neural networks, recurrent neural networks, and transformers. The course will be a combination of lectures, presentations, and machine learning competitions. Renumbered from CSE 154. Students may receive credit for one of the following: CSE 151B, CSE 154, or COGS 181. May be coscheduled with CSE 251B. Recommended preparation: knowledge of Python.
Prerequisites
From the catalog: “MATH 20C or MATH 31BH and BENG 134 or COGS 118D or CSE 103 or ECE 109 or ECON 120A or MAE 108 or MATH 180A or MATH 180B or MATH 181A or MATH 183 or MATH 186 or SE 125 and COGS 118A or COGS 118B or COGS 188 or CSE 151A or DSC 140B or ECE 175A; restricted to students within the CS25, CS26, CS27, CS28, CS29, EC26, and DS25 majors. All other students will be allowed as space permits.”
Where it sits in degree plans
Required in (1)
- Artificial Intelligence
Courses that list CSE 151B as a prerequisite (1)
- COGS 182 Introduction to Reinforcement Learning