Course Syllabus

Neural Network

 

Content

We will understand a number of issues about Deep Neural Networks.  Our goal is to understand the mathematical underpinning of a number of tools in Deep Learning.  We hope that this will give you the foundation to understand and critically think about continued advances in this dynamic area.

List of Topics
Linear Regression
Logistic Regression
Elementary Logic
Backpropagation
Gradient Descent
Feedforward Networks
Testing, Validation, and Training
Feature Importance
Dropout
Batch Normalization
Convolutional Neural Networks
Recurrent Neural Networks
Reinforcement Learning
Generative Adversarial Networks

 

Assessments

 

Component Date/Frequency Percentage
Midterm 1 10/8 15%
Midterm 2 12/5 15%
Individual Theory Assignments Each module (roughly) 25%
Group Coding Assignments Each module (roughly) 25%
Deep Dive Project End of Semester 20%

 

 

Miscellanea