SemesterFall Semester, 2019
DepartmentMA Program of Computer Science, First Year MA Program of Computer Science, Second Year
Course NameDeep Learning:Fundamentals and Applications
InstructorPeng Yan Tsung
Credit3.0
Course TypeElective
Prerequisite
Course Objective
Course Description
Course Schedule

Week 1

Subject:Introduction & syllabus

Covering topics: Introduction to Deep Learning.

Reading: N/A

Teaching/HW: Explain the syllabus

Hours spent for preview: N/A

Hours spent for review:  1 hour



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Week 2 & 3

Subject:Mathematical tools

Covering topics: Linear Algebra and Probability

Reading: Course slides

Teaching/HW: Get familiar with math tools often used in machine learning

Hours spent for preview and review: 2 hours each



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Week 4, 5, & 6

Subject:Machine Learning Basics

Covering topics: linear and logistic regression/classification, SVM, kNN

Reading: Course slides

Teaching/HW: Introduce various machine learning techniques. HW1 will be released. It needs to be turned in within one week after being released.

Hours spent for preview and review: 2 hours each



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Week 7

Subject:Optimization and gradient descent

Covering topics: Convex functions and various gradient descent approaches

Reading: Course slides.

Hours spent for preview and review: 2 hours each



Week 8

Subject:Artificial Neural Networks 

Covering topics: Perceptron, Multilayer Perceptron, Back-propagation

Reading: Course slides

Teaching/HW:  HW2 will be released. It needs to be turned in within one week after being released.

Hours spent for preview and review: 2 hours each



Week 9

Subject: Midterm Exam



Week 10

Subject: Deep learning frameworks

Covering topics: Keras, Tensorflow and Pytorch

Reading: Course slides

Teaching/HW: Get familiar with deep learning frameworks

Hours spent for preview and review: 2 hours each



Week 11 & 12

Subject: Convolutional neural networks

Covering topics: Convolutional neural networks, often-used architectures 

Reading: Course slides

Teaching/HW: Final project will be released. 

Hours spent for preview and review: 2 hours each



Week 13 & 14

Subject: Recurrent neural networks

Covering topics: Recurrent neural networks (RNNs)

Reading: Course slides

Teaching/HW: Teach student RNNs and their underlying math

Hours spent for preview and review: 2 hours each



Week 15 

Subject: Generative adversarial networks (Flexible Teaching)

Covering topics: Introduction to generative adversarial networks

Reading: Course slides

Hours spent for preview and review: 2 hours each



Week 16 & 17

Subject: Deep Learning Applications (Flexible Teaching)

Covering topics: Computer vision and image processing based on deep learning

Reading: Course slides

Teaching/HW: Discuss several problems better solved by deep learning

Hours spent for preview and review: 2 hours each



Week 18: Final Presentation


Teaching Methods
Teaching Assistant
Requirement/Grading

1. Homework (30%) - two homework assignments



2. Midterm Exam (30%)



3. Final Project (40%)


Textbook & Reference

Deep Learning https://www.deeplearningbook.org/

Urls about Course
1. Neural Network and Deep Learning http://neuralnetworksanddeeplearning.com/ 2. Deep Learning: A Practitioner’s Approach https://www.amazon.com/dp/1491914254?tag=inspiredalgor20
Attachment