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IEMS5780 Course Homepage

Final Examination

  • The final examination paper can be obtained here: Final Exam (PDF)
  • Submission deadline: 2019-12-14 (Saturday) 12:00 noon
  • Please use this template to prepare your answers for submission.
  • Please submit the text file to Blackboard before the deadline.
  • Update 2020-04-24: Final examination review notes: FinalExamReview.docx

Course Description

Machine learning refers to making computer to perform various tasks by learning from data. It is also now one of the essential components in many online services, such as in generating personalized recommendations on e-commerce platforms, performing face detection and recognition, predicting the arrival time of delivery, etc. Given the widespread usage of machine learning, it is important that complex machine learning models can be deployed in an efficient way to support real time services at scale and to allow seamless update of the models.

This course will introduce basic concepts in computer networking and network programming, and then go ton to introduce how scalable online services can be created and maintained, with a focus on services that nvolve machine learning. Topics will include asynchronous programming, distributed message queues and brokers, load balancers, micro-services, distributed caches and databases, and challenges and solutions in deploying various machine learning models.

Course Details

Lectures

  • Instructor: Dr. Albert Au Yeung [cmauyeung@ie]
  • Time: Every Friday 19:00 - 21:30
  • Venue: Lee Shau Kee Architecture Building (ARC) G03

Teaching Assistants

  • Mr. Yuming Zhang [zy219@ie] (Office hour: Fri 17:00-19:00 @SHB724)
  • Miss. Zheyuan Yang [yz019@ie] (Office hour: Fri 13:30-15:30 @SHB826A)

Assessment Schemes

  • 10% - Attendance
  • 60% - Programming Assignments
  • 30% - Final Examination

Discussion Channel

References

Python Programming

Machine Learning

Scalability

  • Guide to Reliable Distributed Systems: Building High-Assurance Applications and Cloud-Hosted Services (Kenneth Birman, 2012)
  • Big Data: Principles and best practices of scalable realtime data systems (Nathan Marz & James Warren, 2015)
  • Mining of Massive Datasets (Anand Rajaraman, Jeff Ullman & Jure Leskovec, 2011)
  • Scalability Rules: 50 Principles for Scaling Web Sites (Martin L. Abbott & Michael T. Fisher, 2011)