IE434 Deep Learning: Mathematics and Applications Fall 2023

Section U (undergraduate; CRN 78341) and G (graduate; CRN 78342)

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.

Topics:

  • 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).

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

Logistical notes: