授業の形態
|
|
|
| |
|
アクティブラーニング
|
|
学生が議論する、学生が自身の考えを発表する、学生が文献や資料を調べる
|
| |
|
授業内容と方法
|
(* This class will include Japanese students.)
This class consists of 2 or 3 parts. We learn about a general process to be ready for re-useable knowledges by data mining theories, or about machine learning models as a mining method. Everyday has two or three persons have to explain/introduce about assigned references, basic theory from part 1 and applications from part 2.
According to circumstances (belong to students), we will take consideration time as part 3 for practice about your-own work or interest applications.
|
| |
|
URGCC学習教育目標
|
|
専門性
|
| |
|
達成目標
|
+ You can explain about general process of data mining or a few models in machine learnings.
+ You can explain/introduce about newly conference papers.
If we select part 3,
+ You can try to design/build the process to any your familiar topic.
+ You can evaluate/consider about raw data and results of your application.
|
| |
|
評価基準と評価方法
|
You must explain/introduce/discuss about assigned references.
presentation (50%), presentation documents (20%), Q&A and discussions (30%)
|
| |
|
履修条件
|
Math (especially Linear Algebra and Statistics), experiences of programming.
|
| |
|
授業計画
|
#1 : Guidance
======================================
Part 1: Introduction to data mining or machine learning
#2 : What is Data Mining? Some definitions, simple examples and applications.
#3 : Machine Learning and Statistics, aspects of personal information.
#4 : Instances and Attributes as Input, and Knowledge Representations as Output
#5 : The basic methods and algorithms 1
#6 : The basic methods and algorithms 2
#7 : Credibile experiment designs and evaluation ways
#2 : What is Machine Learning?
#3 : Classification by perceptron learning
#4 : Classification models in Scikit-learn
#5 : Building good training sets and pre-processing
#6 : Compressing data via dimensionality reduction
#7 : Model evaluation and Hyperparameter tuning
#8 : reserve day (e.g., adjustments for Part 2)
======================================
Part 2: Discussion about applications
#9 : readings 1
#10 : readings 2
#11 : readings 3
#12 : readings 4
#13 : readings 5
#14 : readings 6
#15 : readings 7
|
| |
|
事前学習
|
|
read carefully references.
|
| |
|
事後学習
|
|
read carefully references.
|
| |
|
教科書にかかわる情報
|
|
|
| |
|
教科書全体備考
|
|
|
| |
|
参考書にかかわる情報
|
|
|
9780123748560
|
we will read the part 1 of this book to learn the data mining theory.
|
|
Ian H. Witten, Eibe Frank, Mark A. Hall
|
|
Morgan Kaufmann
|
2011
|
|
|
|
1783555130
|
we will read the part 1 of this book to learn the basis of machine learning.
|
|
Sebastian Raschka
|
|
Packt Publishing
|
2015
|
|
|
|
| |
|
参考書全体備考
|
|
|
| |
|
使用言語
|
|
英語
|
| |
|
メッセージ
|
In part 2, we will read some best papers on below conferences in recent 5 years. But I hope that you suggest us any related papers in your interest.
+ mainly application examples.
IEEE/WIC/ACM International Conference on Web Intelligence (IEEE/WIC/ACM WI)
ACM International Conference on Web Search and Data Mining (ACM WSDM)
ACM Special Interest Group on Information Retrieval (SIG-IR)
+ mainly theoretical or technical papers.
IEEE International Conference on Data Mining (ICDM)
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (ACM KDD)
|
| |
|
オフィスアワー
|
room: 705, eng. bldg. #1
office hour: 12:50-14:20 on Thursday (in progress)
|
| |
|
メールアドレス
|
|
tnal@ie.u-ryukyu.ac.jp
|
| |
|
URL
|
|
http://ie.u-ryukyu.ac.jp/~tnal/2018/dm-theory/
|
| |
|
|