深度概率機器學習
Deep Probabilistic Machine Learning
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 深度概率機器學習 CM212 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Deep probabilistic machine learning is about making probabilistic predictions with deep neural networks. It's like providing reliable confidence scores for predictions in a systematic and analytical way. The first part of this course covers Gaussian process regression, Gaussian process classifiers, deep Gaussian process classifiers and their applications, such as time series estimation and forecasting. We will also study financial applications. The second part of this course introduces some modern machine learning topics, such as transformer networks, denoising diffusion models, vision-language pretraining, and weakly supervised learning. The lecture will be delivered in Mandarin. (中文授課) 主要使用Microsoft Teams教學,網址如下: 請注意,將公告於 E3 數位教學平台E3@NYCU 配合數週實體教學
1. Basic machine learning concepts, such as overfitting, underlining, gradient descent, etc.. 2. Basics of probability theory, linear algebra, and multivariable calculus 3. Reasonably computer programming skills in Python/numpy
Websites and reference books. 1. Hennig, P., 2020. Probabilistic Machine Learning. lecture course, University of Tübingen, URL = https://uni-tuebingen.de/en/180804 2. Kevin P. Murphy, Probabilistic Machine Learning: An introduction, MIT Press, 2022, URL = https://probml.github.io/pml-book/book1.html URL = https://probml.github.io/pml-book/book2.html
Ex1. Basic Bayesian inference. (Coding) Ex2. Gaussian linear regression (Coding) Ex3. Gaussian process regression (Coding) Ex4. Integrating Gaussian process classifier and deep neural networks. (Coding) Ex5. Financial Application of Gaussian processes and Bayesian optimization (Coding and Report) Ex6. Integrating CNN and Gaussian process classifier. (Coding and report) Ex7. Transformer networks for object detection (Report) Ex8. Denoising Diffusion Models in computer vision (Report) Ex9. Vision-Language Pretraining (Report)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course outline |
| 第 2 週 | Reasoning under uncertainty |
| 第 3 週 | Monte Carlo sampling |
| 第 4 週 | Gaussian distribution and Gaussian process |
| 第 5 週 | Understanding Kernels |
| 第 6 週 | Example: Financial Application of Gaussian processes |
| 第 7 週 | Gaussian process classification |
| 第 8 週 | Generalized linear model and Exponential families |
| 第 9 週 | Example: Last-layer Laplace approximation |
| 第 10 週 | Review and Report |
| 第 11 週 | Probabilistic classifier integrating deep neural network and Gaussian process (I) |
| 第 12 週 | Probabilistic classifier integrating deep neural network and Gaussian process (II) |
| 第 13 週 | Vision Transformers Object Detection Transformers |
| 第 14 週 | Denoising Diffusion models + GAN + VAE |
| 第 15 週 | Vision-Language Pre-training Concepts disentangle representation |
| 第 16 週 | Term project report |
| 第 17 週 | |
| 第 18 週 |
No textbook for this course.
- 時間
- By appointment
- 聯絡方式
- machingwen@nycu.edu.tw