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
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