Foundations of Neural Networks
Durham
Engineering&Physical Sciences :: Computer Science
Credits: 4.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:
20
CRN: 16261
CRN: 16261
Neural networks are a class of machine learning models which have recently revolutionized many applied machine learning domains such as natural language understanding, image/video processing, bioinformatics, time series analysis. This course teaches students to develop new neural network architectures from scratch and customize them. The course covers all necessary foundations of neural networks including gradient descent optimization and vector calculus. Students will learn how to design models using idioms such as observed variables, latent variables, gate variables and different functions as well as a wide range of state-of-the-art architectures as design examples.
Prerequisite(s): CS 515
Cross listed with : CS 852.01
Instructors: Laura Dietz
Times & Locations
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
8/26/2024 | 12/9/2024 | MW | 3:40pm - 5:00pm | KING N101 |
8/26/2024 | 12/9/2024 | M | 5:10pm - 6:00pm | KING N328 |
Additional Course Details:
This is an implementation-intensive elective.