Welcome to the class website of Learning from Data!
2024-9-10: The review session will be held on Thursday(9/12) evening 7:00-9:00PM, at International Phase 1, Room C501.
Class info
Time: Friday 9:50am-12:15pm
Location: International Phase 1 (国际一期) C405A
This introductory course gives an overview of many concepts, techniques, and algorithms in machine learning, from linear models such as logistic regression and SVM to more advanced topics such as deep neural networks and reinforcement learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
For more information about grading, homework and exam policies, see the class syllabus
Prerequisites: Undergraduate level calculus, probability and linear algebra. Basic Python programming.
✨Q&A Document✨: This semester we encourage students to ask questions and discuss together on the online document. Come and join the society 😊
Team
Yang Li Instructor
Weida Wang Head TA
Yuanbo Tang TA
Tong Wu TA
Chengfeng Wu TA
Office hours
Name | Time | Location |
Yang | Monday 2:00-4:00pm | Info Building 1108a |
Weida | Thursday 5:00-6:00pm | Info Building, 11th floor common area |
Yuanbo | Wednesday 5:00-6:00pm | Info Building, 11th floor common area |
Tong | Friday 5:00-6:00pm | Info Building, 11th floor common area |
You can also make appointments outside office hours.
Recitation & Review Sessions
Recitations will be held every Friday in the lecture room.
Date | Topic | Reference |
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9/12 |
Review Session: Basic linear algebra and probability; Scientific programming in Python.
(Probability Theory, Coding Prerequisites, Python Tutorial | Course Video)
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The Maxtrix Cookbook by KB Petersen.
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9/27 |
Recitaiton: Two example of GLM.
(GLM | Course Video)
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10/11 |
Recitaiton: PA1 Q&A, KKT Condition, Matrix Derivative
(Course Materials & Video)
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10/18 |
Recitaiton: Backpropagation & Forward-Forward Algorithm
(Course Material1 | Course Material2 | Video)
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Forward-Forward Algorithm by Geoffrey Hinton |
Class Schedule
The main reference reading material is the CS229 Machine Learning Lecture Notes (MLLN) by Andrew Ng and Tengyu Ma
Date | Topic | Readings & References | Homework Release |
---|---|---|---|
9/13 | Introduction (Slides) |
Written Assignment 0 (don't need to submit)
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9/20 |
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"Supervised learning" 1.1-1.3,2.1-2.3 (MLLN) Convex functions by Boyd & Vandenberghe (see Chapter 3)
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Programming Assignment 1
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9/27 | "Generalized linear models" (MLLN) Generalized Linear Models by Nelder & Wedderburn (1972)
"Generative learning algorithms" (MLLN)
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Written Assignment 1
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10/11 |
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"Support Vector Machines" (MLLN) References: Support Vector Networks by Cortes and Vapnik; SVM notes from “Selected Applications of Convex Optimization” by Li Li; Convex optimization by Stephen Boyd (See Chapter 5)
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Programming Assignment 2
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10/18 | "Support Vector Machines" (MLLN) “Deep Feed Forword Networks” from Deep Learing by Ian Goodfellow
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Written Assignment 2, WA1 solution
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