PRML-Fall22-FDU
Course website for PRML Fall 2022 at Fudan University (Github Link).
Course Logistics
- Instructors: Prof. Xipeng Qiu, Prof. Yugang Jiang
- TAs: Peng Li
- Time: Monday 6:30 pm - 9:05 pm
- Venue: HGX0502 / Online
- Prerequisites: College Calculus, Linear Algebra, Probability and Statistics, Numerical Optimization and Python Programming
- TextBooks：
- Neural Network and Deep Learning, Xipeng Qiu, online version
- Neural Network and Deep Learning in Practice, Xipeng Qiu, Paddle Team, online version
- Pattern Recognition and Machine Learning, Christopher M. Bishop, online version
- Grading: 3 assignments with a total 45% weight, and a 50% final project, and 5% for class.
- Previous Years: Spring 2022 / Spring 2021 / Spring 2020 / Spring 2019
News
- [05/09] Welcome to PRML 2022 Fall!
Schedule
| Date | Description | Course Materials | Events | DDLs |
|---|---|---|---|---|
| 05/09 | Lec1: Introduction | Further Reading: [ICLR20] Deep Double Descent |
Exercise: Deep Learning Hardware and Software, Python Numpy Tutorial, Paddle Tutorial |
|
| 12/09 | Cancelled for Mid-Autumn Festival | |||
| 19/09 | Lec2: Linear Regression | Video: Linear Regression Further Reading: [BOOK] The Matrix Cookbook, [ICML17] Influence Function, [CVPR21] Torch.manual_seed(3407) is all you need |
Exercise: Linear and Polynomial Regression |
|
| Lec3: K-NN and Decision Tree | ||||
| Lec4: Perceptron and Logistic Regression | ||||
| Lec5: Kernel Method and SVM | ||||
| Lec6: Feedforward Neural Networks | ||||
| Lec7: Convolutional Neural Networks | ||||
| Lec8: Recurrent Neural Networks | ||||
| Lec9: Attention Mechanism | ||||
| Lec10: Unsupervised Learning | ||||
| Lec11: Model-Independent Machine Learning | ||||
| Lec12: Guest Lecture |
Coursework
Guidelines
Different from previous years, we will mainly use the PaddlePaddle AI Studio platform this year for our programming exercises. These exercises will teach you to implement the machine learning models you learned step by step and conduct some basic explorations. In addtion to these exercises, several more interesting assignments and a final project will be designed for you to practice using what you have learned to solve some real-world problems. Notice that we will only grade the assignments and the final project, and leave the exercises just as study materials.
These assignments and the final project will be released both on the e-learning platform and this website. But you have to submit them through the e-learning platform.
Late Policy: Everyone of you have 2 free late days for this semester. You can only use the late days for your assignments but not for the final project report. Once you have exhausted your free late days, we will deduct a late penalty of 25% per additional late day.