機器學習之訊號處理應用
Machine Learning for Signal Processing
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 機器學習之訊號處理應用 ED202（光復） 2 節連堂 |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
In this course, we would discuss the connection between signal processing and machine learning. Specifically, we would focus on applying machine learning methods for signal processing. We will cover the fundamental concepts and methods of signal processing and machine learning, which are useful to solve practical engineering problems. Students will learn contemporary techniques for capturing signals, processing signals, enhancing signals, classifying signals, and learning from signals. The topics include mathematical models for discrete-time signals, Hilbert spaces, signal transformation and representation, time-frequency analysis, linear and non-linear processing, signal classification and prediction, factor components, basic image processing. With time, we may illustrate some advanced applications related to compressed sensing and deep learning at the end of the course. Note that we lecture content would change from time to time in order to provide better learning experience.
Signals and systems、Linear algebra、Probability、Programming skills
無備註
TA Office: EC118 TAs: 張竣傑(spaw06j0@gmail.com )、原瑄(yuan040686@gmail.com)、賴欣儀(laisy.ee10@nycu.edu.tw) Online Course: https://nycu.webex.com/meet/chingchun ACM Lab Website: http://acm.cs.nctu.edu.tw
Temporary Plan: (1) Exercises/Homework 50%, (2) Final Project Proposal 20%, (3) Final Project/Paper Format Report/System Demo/ Challenge 30%.
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction, Review of DSP 2023-02-13(一) |
| 第 2 週 | Perception and feature audition and vision 2023-02-20(一) |
| 第 3 週 | Learn to extract features by PCA 2023-02-27(一) |
| 第 4 週 | Independent component analysis and non-negative decomposition 2023-03-06(一) |
| 第 5 週 | Independent component analysis and non-negative decomposition 2023-03-13(一) |
| 第 6 週 | Nonlinear dimension reduction and representation 2023-03-20(一) |
| 第 7 週 | Nonlinear dimension reduction and representation 2023-03-27(一) |
| 第 8 週 | Supervised Classification 2023-04-03(一) |
| 第 9 週 | Advanced Supervised learning 2023-04-10(一) |
| 第 10 週 | Clustering K-means, GMMs, Spectral, EM 2023-04-17(一) |
| 第 11 週 | Signal Classification 2023-04-24(一) |
| 第 12 週 | Clustering K-means, GMMs, Spectral, EM 2023-05-01(一) |
| 第 13 週 | Compressive Sensing 2023-05-08(一) |
| 第 14 週 | Signal Separation 2023-05-15(一) |
| 第 15 週 | Deep Learning 2023-05-22(一) |
| 第 16 週 | Advanced Topics 2023-05-29(一) |
| 第 17 週 | 2023-06-05(一) |
| 第 18 週 | 2023-06-12(一) |
1.“Machine Learning: A Probabilistic Perspective”, Kevin P. Murphy, MIT Press, 2012/08/24 2. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer, 2008 3. "Machine Learning for Signal Processing Data Science, Algorithms, and Computational Statistics", Max A. Little, Oxford University Press, 2019
- 地點
- EC708
- 時間
- Tuesday, 10:00 ~ 12:00
- 聯絡方式
- chingchun@nycu.edu.tw