Web Data Engineering 1
Time: Monday 1st Hours
Toyohashi University of Technology, Computer Science and Engineering
Teacher: Prof. Masaki Aono
Day by day, a massive amount of data has been generated, accumulated, and updated on the Internet, where data include texts, images, sounds, movies, 2D/3D shapes, numeric values, and their composites. Extracting important pieces of information is crucial in many Closed/Open
The objectives of this lecture is to demonstrate the state-of-the art technologies in data science ranging from
data representation, data mining, text mining, natural language processing, information retrieval, information extraction, machine learning (including both unsupervised and supervised learning with/without deep learning frameworks), based on fundamental data science technologies.
The key to Moodle is aono-wd2019.
Assignments are supposed to be submitted from Moodle.
Questions are always welcome. Please send an email to my mail address as below:
Introduction (Basics of Data Science: Data Representation and Basic Statistics)
|２||April 15||Statistics and Basic Machine Learning Technologies
|３||April 22||Information Retrieval (Search, Similarity, Language Model, Dimensional Reduction, Evaluations)
|４||May 13||Web Mining including Web Link Analysis and Content Mining
|５||May 20||Unsupervised and Supervised Learning (1)
|6||May 27||Supervised and Unsupervised Learning (2)
|7||June 3||Multimedia Feature Extraction, Search, Classification, and Deep Learning
|8||June 10||Final Exam (Written Exam)
Evaluation of Achievement
In principle, for those who have attended all the classes, the credit will be given as follows:
Assignments (20%) and Final exam (80%)
S: (>=90), A: (>=80), B: (>=70), C: (>= 60)
Credit 1 (1st Quarter)
Nothing in particular
Materials for this class will be availlable at https://www.kde.cs.tut.ac.jp/~aono/myLecture.html.
Gloals to be Achieved
- Able to implement and apply fundamental data science (mining) technologies.
- Able to understand fundamental technologies of information retrieval such as natural language processing, search performance measures, feature extraction, and ranking methods such as language model
- Able to understand basics of machine learning (supervised and unsupervised learning) and deep learning
- Able to understand basics of Web link analysis, Web content mining, Time series data mining