Announcements
Feb 17th, 2017: Please note that the class starts at 14:20pm-16:45pm every Monday at Six-Jiao 6B403.
Jan 4th, 2017: Course site is up. Potential students please come back and check
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Course Instructor Course Assistants Class Time and Location
Course Description
This course is a graduate course and is primarily project-oriented. This course teaches how to use machine learning techniques to build Intelligent Operations System (IOS) systems for the Internet. The high-level objectives for these systems are that for the targeted networks/applications in the Internet:
1) What happened in the past can be reconstructed automatically and accurately;
2) What’s going on now can be detected/inferred accurately to trigger automatic mitigation or suggest immediate actions to the operators;
3) What will happen in the future can be predicted with high confidence.
IOS is at the intersection of machine learning, engineering, and systems, as illustrated in the below figure.
Through case studies based on recent research papers in top conferences, this course will cover the latest research progress in Intelligent Operations, and how the latest techniques (time series analysis, machine learning, deep learning, big data systems, streaming systems) can be applied to the field of Intelligent Operations.
Grading Policies
Attendance: 10%; Project: 90%
Course Information
by James F. Kurose, Keith W. Ross.
Statistical Data Mining Tutorials
by Andrew Moore.
《Data Science for Business–What you need to know about data mining and data-analytical thinking》Foster Provost & Tom Fawcett
《Deep Learning》 by Ian Goodfellow, Yoshua Bengio, Aaron Courville
《Site Reliability Engineering –How Google Runs Production Systems》, by Betsy Beyer, Chris Jones, Jennifer Petoff & Niall Richard Murphy