資料探勘專題
Special Topics on Data Mining
| 節 | 週二 |
|---|---|
A 18:30–19:20 | 資料探勘專題 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
課程概述與目標：The main objective of this class is to explore research topics on techniques and applications of data mining, drawing work from areas including database technology, artificial intelligence, machine learning and knowledge-based systems. Selected research papers from conference proceedings and journals will be discussed. The course will cover research topics relating to time series predictions, classification, clustering, text mining, deep learning and recommender systems. Moreover, the course emphasizes on the practices of applying data mining techniques to various applications and big data analytics. Core research skills of literature analysis, innovation, evaluation of new ideas, and communication are emphasized via paper presentation and discussion.
Data Mining Research & Practices
1、Inclusive of visiting institutes/organizations outside the NCTU or other academic events. 2、Please adhere to pertinent regulations/laws on intellectual property rights. Do not use pirated textbooks.
Homework (20%), Paper Survey and Project (20%), Presentation & Discussion (40%), Others (20%)
- Recommender systems
- Text Mining & Knowledge Engineering
- Classification & Prediction
- Deep Learning
- Big Data Analytics
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview |
| 第 2 週 | Content-based filtering Collaborative filtering |
| 第 3 週 | Hybrid Matrix factorization |
| 第 4 週 | Text mining |
| 第 5 週 | Information retrieval & amp filtering |
| 第 6 週 | Document classification |
| 第 7 週 | Document recommendation |
| 第 8 週 | Classification |
| 第 9 週 | Support Vector Machine Random forest |
| 第 10 週 | Mid-Presentation & amp Report |
| 第 11 週 | Prediction Regression |
| 第 12 週 | Deep learning |
| 第 13 週 | CNN, RNN |
| 第 14 週 | LSTM, GAN |
| 第 15 週 | Deep learning & amp Recommendation |
| 第 16 週 | Big data analytics Hadoop & amp Spark |
| 第 17 週 | Final Presentation & amp Report |
| 第 18 週 | Project Demo |
1. Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, 2011, by Jiawei Han and Micheline Kamber 2. Introduction to Data Mining, 2nd, Pearson, 2019, Pang-Ning Tan, Michael Steinbach, Anju Karpatne and Vipin Kumar 3. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2017, Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal 4. Paper Readings
- 地點
- MB305
- 時間
- Tuesday Pm 5:30 ~ 6:30
- 聯絡方式
- dliu@mail.nctu.edu.tw