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
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 3, 4, 5 (Holiday)
Subject： Machine Learning Basics
Covering topics: linear and logistic regression/classification
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 6
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 7, 8
Subject： Artificial Neural Networks and Deep learning frameworks
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 & 11
Subject: Convolutional neural networks and training techniques
Covering topics: Convolutional neural networks, often-used architectures and training techniques
Reading: Course slides
Teaching/HW: The final project will be announced
Hours spent for preview and review: 2 hours each
Week 12 & 13
Subject: Recurrent neural networks & Transformers
Covering topics: Recurrent neural networks (RNNs)
Reading: Course slides
Teaching/HW: Teach students RNNs and their underlying math
Hours spent for preview and review: 2 hours each
Week 14 & 15
Subject: Generative AI (Flexible Teaching)
Covering topics: Introduction to generative models, including VAE, GAN, and Diffusion models
Reading: Course slides
Hours spent for preview and review: 2 hours each
Week 16 & 17
Subject: Reinforcement Learning, Large Language Model, Deep Learning Applications (Flexible Teaching)
Covering topics: Policy gradient, Actor-Critic Network, Q Network, Computer vision, and image processing applications
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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