Type of Credit: Elective
Credit(s)
Number of Students
Text mining is an attractive area in artificial intelligence today. With the progress of machine learning, the power of text mining has been shown in novel applications in various domains. In the business area, text mining helps businesses to discover useful information from large and heterogeneous data, solve the information overload problem, and create value for organizations and societies.
The first part of the course will introduce the components and techniques of text mining. Then, after the midterm exam, the students will learn the applications of text mining in various topics and handle textual data to solve business problems in every-week labs. The students are expected to analyze real-world data with text mining techniques and provide insights into the final project.
能力項目說明
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
|
Week |
Subject |
|
1 |
|
|
2 |
|
|
3 |
|
|
4 |
classifier, feature extraction (BOW, naïve bayes model, logistic regression, decision tree…), overfitting, etc.
|
|
5 |
|
|
6 |
|
|
7 |
Talk (Alternative class) |
|
8 |
review (彈性授課) |
|
9 |
Midterm-exam |
|
10 |
Introduction to text mining applications |
|
11 |
Topic: voice recognition and sentiment analysis |
|
12 |
seminar |
|
13 |
Topic: auto summarization, auto correction, and auto translation |
|
14 |
seminar |
|
15 |
Topic: chat robot, virtual assistant (or recommender systems) and social listening |
|
16 |
review (彈性授課） |
|
17 |
Final project presentation |
|
18 |
Final-exam |
|
30% |
Exams |
30% Lab / Assignments |
|
|
|
30% |
Project |
10% |
Participation |
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Possible venue: https://aidea-web.tw/aicup_meddialog
A team should consist of 3 to 5 members
- Innovation
- Analysis of the results