Seminar on Internet Technologies (Summer 2017)

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Details

Workload/ECTS Credits: 5 ECTS (BSc/MSc AI); 5 (ITIS)
Module: M.Inf.1124 -or- B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
Lecturer: Dr. Hong Huang
Teaching assistant: []
Time: Apr 20, 16:00ct: Introduction Meeting
Place: IFI Building, Room 3.101
UniVZ [1]


Course description

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Passing requirements

  • Actively and frequently participate in the project communication with your topic advisor. Topic advisor has the right to decide whether a student is eligible for the final presentation.
    • This accounts for 20% of your grade.
  • Present the selected topic (20 min. presentation + 10 min. Q&A).
    • This accounts for 40% of your grade.
  • Write a report on the selected topic (12-15 pages) (LaTeX Template:[2]).
    • This accounts for 40% of your grade.
  • Please check the #Schedule and adhere to it.

Schedule

  • Apr. 20, 16:00ct: Introduction meeting
  • TBA : Deadline for registration
  • TBA : Presentations
  • September. 30, 2017, 23:59: Deadline for submission of report (should be sent to the topic advisor!)


Topics

Topic Topic Advisor Initial Readings
Deep into Google Translate

This study is to provide a comprehensive study of one of the Google products - Google translate and aim to understand the technologies behind it.

Hong Huang [3]
Inferring social capital from big data

This study is to discover the state of art of social capital measuring, particularly, from big data perspective.

Hong Huang [4][5]
An overview on deep learning framework

In this work, you will be asked to do a survey on all popular deep learning framework either in academe or industry, like tensorflow, caffe and so on. You shall elaborate their shortcomings and advantages.

Hong Huang [6]
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