ASRM 499, 595 Fall 2023

ASRM 499 Section UDL (undergraduate; CRN 77131) and ASRM 595 GDL (graduate; CRN 78325)

Neural Network

The goal of this class is to understand some basic ideas of deep neural networks. We will understand how they work and apply them to sample datasets.


  • Linear Regression
  • Logistic Regression
  • Elementary Logic
  • Backpropagation
  • Gradient Descent
  • Feedforward Networks
  • Testing, Validation and Training
  • Advanced architectures: some combination of
    • Recurrent Neural Networks (including LSTM’s)
    • CNN’s
    • Reinforcement Learning

Grading policy: Final grades will be determined on the basis
of the total numerical score (and will be curved).

Hourly Exam (10/3)15% of grade
Hourly Exam (11/14)15% of grade
Homework55% of grade 
Final Project15% of grade

Extra credit may be available.

Logistical notes: