Big Data Analytics
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Lecturer: Shyi-Chyi Cheng (鄭錫齊)
Email: csc@mail.ntou.edu.tw
Phone: (02)24622192 #6653
Webpage: https://cse.ntou.edu.tw/p/412-1063-7765.php?Lang=zh-tw
Course ID: M18012TT
Credits: 3
Objective:
This course introduces the fundamentals of big data analytics. The advance topics on machine learning and deep learning for bid data analytics and their emerging applications are discussed in the course. Another focus of the course is the discussion of digital twin frameworks for industry 4.0 and artificial intelligent applications.
Course Prerequisites: None
Outline:
Chapter 1: An introduction to big data analytics
Chapter 2: Fundamentals of big data analytics
Chapter 3: Digital-twin based Internet of Things systems
Chapter 4: Similar and frequent items fast seraching algorithms
Chapter 5: Time-series data analytics
Chapter 6: Recommendation systems
Chapter 7: Deep learning based computer vision
Chapter 8: Deep learning based large language models
Teaching Method: On class lecturing, project reporting, and Homework practicing
Reference:
Anand Rajaraman, Jure Leskovec, and Jeffrey D. Ullman, Mining of Massive Datasets, Stanford Univ. USA.
Lecture notes & Articles from related journals and conferences
Course Schedule (subject to change):
Chapter 1: An introduction to big data analytics (one weeks)
Chapter 2: Fundamentals of big data analytics (two weeks)
Chapter 3: Digital-twin based Internet of Things systems (two weeks)
Chapter 4: Similar and frequent items fast seraching algorithms (two weeks)
Chapter 5: Time-series data analytics (two weeks)
Chapter 6: Recommendation systems (two weeks)
Chapter 7: Deep learning based computer vision (two weeks)
Chapter 8: Deep learning based large language models (two weeks)
Evaluation:
Midterm exam: 30%
Project report: 30%
Homework: 40%
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