Spring 2020

Semester Starts!

The semester starts on Jan 6th, 2020.

CIS 5930-0003 Deep Learning and Applications (syllabus)

Course Information

CIS 5930-03 Deep Learning and Applications
Date: Jan 6-Apr 24, 2020
Time: M/W 3:35-4:50PM
Location: Lov 0103

Instructor Information

Instructor: Jiawei Zhang
Email: jzhang [AT] cs.fsu.edu
Office: 169 James Love Building
Office Hours: M/W 5:00-6:00PM

Teaching Assistant: Yuxiang Ren
Email: yren@cs.fsu.edu
Office Hours: NA

Course Description

The course on Deep Learning and Applications focuses on selective areas of importance in data mining, machine learning and deep learning. Data mining, machine learning and deep learning are all the recently emerged hot topic in AI studies, and solutions developed in the research hold substantial impacts in many important applications. Selective topics will be covered in the Deep Learning and Applications course.

Topics Covered

  • Machine Learning & Stochastic Optimization
  • Graph Mining
  • Computer Vision
  • Text Mining
  • Recommender System
  • Misc.


No required textbook.


You are expected to have background knowledge in Data Structure, Algorithm, Discrete Mathematics. You will also need to be familiar with basic Linear Algebra, basic Statistics, and can master at least one programming language and have programming experiences.

Course Format

The objective of this course is to familiarize students with the latest research topics related to data mining and machine learning. Course activities include 1) paper reading and paper review; 2) paper presentation and discussion; and 3) research oriented course paper writing.

  • Paper Reading and Paper Review : Each class will discuss one academic paper. Before class, the students should read the paper to be presented in class, and write a short review (no longer than 1 page) for the paper. The review should cover: (a) a summary of the paper; (b) 3 strong points of the paper; (c) 3 weakness of the paper; (d) potential ideas of future works; and (e) questions about the paper you would like to ask the presenter. Students need to submit the review before class starts. Some external reference papers/articles can be provided for students to understand the papers presented in class (no need to present or write reviews for external reference papers).
  • Paper Presentation and Discussion : Each student will present 2 papers selected during the period of this course, and student will not need to write the reviews of papers you choose to present. Paper presentation is expected to take 45-50 minutes to cover (a) background knowledge, (b) problem description, (c) definitions, (d) ideas, (e) proposed methods and techniques, (f) experiments, (g) results, (h) related works, and (i) potential future works, etc. Additionally, we will have 15-20 minutes for in-depth discussion about the paper and Q&A with the audiences. Students can have Q&A during and after the presentation. Student needs to submit the slides before the end of presentation day.
  • Paper Proposal and Paper Writing : Each student needs to finish an independent research-oriented academic paper in this course, based on the problems studied in this course about data mining and machine learning. The paper should be original work, not recycled published/submitted/on-going work with another faculty or classes. Student needs to submit a paper proposal (no longer than 2 pages) at the mid of the class to provide the planning for the paper. By the end of this semester of this course, student needs to submit the final paper (10 pages in ACM double-column format). Student needs to submit the proposal and final paper before the deadlines.

Course Resources

Presentation Schedule and Progress

Schedule Sheet Link

Reference List


16/16 Weeks


26/26 Papers

Final Paper

Final Paper Due

Grading Policy

In-class presentation: 30% . Powerpoint presentation needs to be submitted on the day of the presentation, before 11:59PM (midnight) of your presentation day. Copying existing presentation from the web is regarded as plagiarism.
Course participation and Q&A: 20% . A summary/review of each in-class discussion paper needs to be submitted before each class starts (i.e., before 3:35PM M/W). During the class, presenter and audiences can have Q&A with the pre-prepared questions in the review report.
Course paper: 50% . Single authored original work on data mining/machine learning. Not recycled published/submitted/on-going work with another faculties or classes.

  • Paper proposal: 10%. Due on March 13, 2020, 11:59PM (midnight).
  • Final paper: 40%. Due on April 24, 2020, 11:59PM (midnight).
Final Grade
  • A: [100-90) A-: [90-85);
  • B+ [85-80), B: [80-75), B-: [75-70);
  • C: [70-60);
  • F: [60-0].

Late Submission Policy

  • Late paper review submission will not be accepted. During the semester, you can miss up to 2 paper reviews without penalty.
  • Late presentation slides submission will not be accepted.
  • Late proposal submission will not be accepted.
  • Late final paper submission will get your grade * 1(t<=24)e^(-(ln (2)/12) t) , if you are t hours late.

Other Issues

Gemeral Policy

  • University Attendance Policy: Excused absences include documented illness, deaths in the family and other documented crises, call to active military duty or jury duty, religious holy days, and official University activities. These absences will be accommodated in a way that does not arbitrarily penalize students who have a valid excuse. Consideration will also be given to students whose dependent children experience serious illness.
  • Academic Honor Policy: The Florida State University Academic Honor Policy outlines the University's expectations for the integrity of students' academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Students are responsible for reading the Academic Honor Policy and for living up to their pledge to "...be honest and truthful and... [to] strive for personal and institutional integrity at Florida State University."
  • Syllabus Change Policy: Except for changes that substantially affect implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice.
Collaboration/Academic Honesty: All course participants must adhere to the academic honor code of FSU which is available in the student handbook. All instances of academic dishonesty will be reported to the university. Every student must write his/her own assignment/code. Showing your code or assignment to others is a violation of academic honesty. It is your responsibility to ensure that others cannot access your code or assignment. Consulting related textbooks, papers and information available on Internet for your assignment and homework is fine. However, copying a large portion of such information will be considered as academic dishonesty. If you borrow a small piece of any such information, please acknowledge that in your assignment. Please see the following web site for a complete explanation of the Academic Honor Code.
Student Disabilities: Americans With Disabilities Act: Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Student Disability Resource Center; (2) bring a letter to the instructor indicating the need for accommodation and what type. This syllabus and other class materials are available in alternative format upon request. For more information about services available to FSU students with disabilities, contact the: Student Disability Resource Center: 874 Traditions Way, 108 Student Services Building, Florida State University, Tallahassee, FL 32306-4167. (850) 644-9566 (voice), (850) 644-8504 (TDD), sdrc@admin.fsu.edu, http://www.disabilitycenter.fsu.edu/.