Syllabus

Lectures and Exercises

For details about the program see the description of individual lectures.

Grading

Course grades will be determined on the basis of two assignments and a final project. The breakdown is shown below:

• Assignment 1: 20%
• Assignment 2: 20%
• Final Project: 60%
• Canvas Discussion Participation (link): up to 5 bonus points

General Schedule

On Course Canvas.

Syllabus

Week 1: Machine Learning Review and Introduction to Deep Learning

Week 2: Multilayer Perceptron, Backpropagation, and GPU Computing

Week 3: Convolutional Neural Networks (CNNs)

Week 4: Recurrent Neural Networks (RNNs)

Week 5: Advanced Topics in Deep Learning

Week 6: Theories of Deep Learning

Week 7: Conclusion / Final Project Presentation

Academic Integrity

Students must properly cite all submitted work appropriately see handbook

Disciplinary actions for “committing an act of academic dishonesty, such as plagiarism, cheating, or falsification of research results” see Section 6 are severe and typically will result in the requirement to withdraw from the program.

Students must attend all classes unless they have permission from the instructor.