資料探勘研究與實務

Data Mining Research & Practice

學期

109-1

學分

3 學分

當期課號

5649

永久課號

IIM5237

開課單位

管理學院碩士在職專班-資管組

授課教師

劉敦仁

校區

光復

類別

選修

上課時間表

節	週四
A 18:30–19:20	資料探勘研究與實務 MB311 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

概述

The main objective of this class is to study techniques and applications of data mining, drawing work from areas including database technology, artificial intelligence, and knowledge-based systems. This course will cover Hadoop, MapReduce and Data Mining, as well as some topics related to Text Mining and Recommender Systems. Students are required to accomplish project assignments on the implementation and experiment on mining data from various application domains.

先修科目

無

教學方式

Reference: Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2017, Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal Tensforflow+Keras深度學習人工智慧實務應用,博碩,林大貴著2017年 Python+Spark 2.0+Hadoop 機器學習與大數據分析實戰,博碩,林大貴著2016年9月 Introduction to Information Retrieval, Cambridge University Press, 2008, Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze (http://nlp.stanford.edu/IR-book/)

評分方式

Homework and Participation (25%), Project (15%), Midterm (30%), Final (30%)

課程大綱

Introduction
Data Preprocessing
Mining Association Rules
Classification
Cluster Analysis
Text Mining
Recommender Systems
Link Analysis and Social Network Analysis
Deep Learning
Big data Analytics

週次計畫

週次	主題
第 1 週	Introduction; Data Preprocessing
第 2 週	Classification and Prediction
第 3 週	Classification and Prediction
第 4 週	Cluster Analysis
第 5 週	Text Mining and Information Retrieval
第 6 週	Text Mining and Information Retrieval
第 7 週	Recommender Systems
第 8 週	Big Data – Platform and Analytics – Hadoop, MapReduce
第 9 週	Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), LSTM
第 10 週	Midterm
第 11 週	Recommender Systems
第 12 週	Classification and Prediction
第 13 週	Classification and Prediction
第 14 週	Clustering
第 15 週	Link analysis and Social Network Analysis
第 16 週	Mining Association Rules, Sequential pattern
第 17 週	Mining Association Rules, Sequential pattern
第 18 週	Final exam

教科書

Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, 2011, by Jiawei Han, Micheline Kamber and Jian Pei (Course slides: http://web.engr.illinois.edu/~hanj/bk3/) Introduction to Data Mining, 2nd, Pearson, 2019, Pang-Ning Tan, Michael Steinbach, Anju Karpatne and Vipin Kumar (https://www-users.cs.umn.edu/~kumar001/ dmbook/index.php) Paper Readings; Competition data sets: https://www.kaggle.com/competitions scikit-learn: Machine Learning in Python http://scikit-learn.org/stable/ Links to Data Mining Software and Data Sets. URL: http://www-users.cs.umn.edu/~kumar/dmbook/resources.htm

Office Hours

地點: MB 305
時間: Thur. pm 5:30 ~ 6:30
聯絡方式: dliu@mail.nctu.edu.tw