資料科學
Data Science
| 節 | 週二 |
|---|---|
2 09:00–09:50 | 資料科學 ED203 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
今年的資料科學與之前不太一樣,首先我們"不會"教基本的deep learning/data mining,希望來修課的同學都已經修過了,這樣可以較深入探討一些新的技術。其次,資料科學非常廣,沒有辦法都教到,這學期除了一開始教Data crawling (怎麼從網路上爬資料),我們只有三大主題: 1) Computer Vision (電腦視覺) 2) Natural Language Processing (自然語言處理) 3) Social Network Analysis (社群網路分析) 每個主題會進行大約五週,希望大家有更深入的了解。
Prerequisites: Machine Learning and Programming (C++, JAVA, or Python) *Knowledge of Python will be useful for the assignments
Midterm: 30% Final Project (in groups): 25% Homework : 45% (5 @ 9% each) Class participation (up to 6 points)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Data Science |
| 第 2 週 | Data Crawling [HW1: Crawling Releases] |
| 第 3 週 | Traditional Computer Vision (I) |
| 第 4 週 | Traditional Computer Vision (II) [HW2: Panorama Releases] |
| 第 5 週 | Advanced Computer Vision (I): Generative Adversarial Networks and variants |
| 第 6 週 | Advanced Computer Vision (II): Other Generative Model- VAE and Flow-based |
| 第 7 週 | Advanced Computer Vision (III): Crowd Estimation and Style Transfer (HW3: GAN Releases) |
| 第 8 週 | Natural Language Processing (I): Word Representation |
| 第 9 週 | Midterm |
| 第 10 週 | Natural Language Processing (II): Sentiment Analysis |
| 第 11 週 | Natural Language Processing (III): Semantic Analysis (HW4: Sentiment Releases) |
| 第 12 週 | Natural Language Processing (IV): Text Generation |
| 第 13 週 | Natural Language Processing (V): Question Answering |
| 第 14 週 | Social Network Analysis (I): Network Characteristics |
| 第 15 週 | Social Network Analysis (II): User modeling and graph embedding (HW5: User Classification Releases) |
| 第 16 週 | Social Network Analysis (III): Recommendation system |
| 第 17 週 | Social Network Analysis (IV): Fake news on social media |
| 第 18 週 | Final Project Presentation |
There is no single textbook. However, a couple of books that are useful and helpful are listed below: (1) Introduction to Data Mining, by P.-N. Tan, M. Steinbach, V. Kumar, 2005 (ISBN:0321321367) (2) Doing Data Science, by C. O'Neil and R. Schutt, 2013 (ISBN: 978-1-4493-5865-5) (3) Python for Data Analysis, by W. McKinney, 2012 (ISBN: 978-1-4493-1979-3)
- 地點
- ED-807
- 時間
- EF, every Tuesday
- 聯絡方式
- TEL:(03)571-2121#54530 EMAIL: hhshuai@nctu.edu.tw