Astronomy 598 Topics in Theoretical Astrophysics (Deep Learning for Computational Scientists)

Winter 2019: Fri 11:00-12:20 Physics/Astronomy Building A214

Instructor: Pramod Gupta
psgupta *at* astro. washington. edu

Office hours: after class, or email.

Web-site: http://vpl.astro.washington.edu/users/psgupta/astro598deeplearning.html

Syllabus:

We will cover most of part I and II of the textbook.

Part I: Applied Math and Machine Learning Basics

(2) Linear Algebra
(3) Probability and Information Theory
(4) Numerical Computation
(5) Machine Learning Basics

Part II: Modern Practical Deep Networks

(6) Deep Feedforward Networks
(7) Regularization for Deep Learning
(8) Optimization for Training Deep Models
(9) Convolutional Networks

Grades:

100% of grade will be based on a project and a presentation.

Textbook:

Deep Learning
Goodfellow, Bengio, Courville
https://www.deeplearningbook.org/