This course introduces methods on neural networks and deep learning, covering basic machine learning concepts and neural network models, model training and testing, and their applications in image recognition, language processing, and robotics.
- Instructor: Stan Z. Li and Tao Lin
- Time: Thursday 8:50am - 10:35am
- Location: YunGu E10-306 (in person)
- Teaching Assistants: Zicheng Liu, Jun Xia, Haitao Lin, Lirong Wu
- Details: Course Schedule for Course Schedule
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We will be using Numpy and PyTorch in this class, so you will need to be able to program in python3.
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You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.
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Weekly Homeworks (20%)
- There are ten weekly homework assignments, each worth 2%.
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Project Proposal (30%)
- You need to make a project proposal. You are encouraged to start early!
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Project Presentation (50%)
- You need to make a presentation on the project and submit the associated reports, slides and codes.
[1] Pattern Recognition and Machine Learning, by Christopher Bishop.
[2] Deep Learning, by I. Goodfellow, Y. Bengio, A. Courville.
[3] Dive Into Deep Learning，by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola.
[4] Neural Networks and Deep Learning, by Michael Nielsen.