General Information
More than ever before, information technology specialists need a deeper understanding of their field.
CSIT Graduate Manual
ITEC 5001, Seminar
- The schedule for ITEC-5001 seminars can be found here.
- Students must sign the attendance sheet to record their ITEC seminar attendance.
- Attending ITEC 5001 seminars does not require registration. In fact, you should register after attending 8 seminars.
- For external events, please inquire in advance with the seminar coordinator if the event can count towards ITEC seminar attendance. If so, students must submit a proof of attendance via an attendance form signed by the event organizer.
- CLUE events are preapproved. The CLUE attendance sheet is shared with us, no need for another proof of attendance.
- If you would like to present in an ITEC seminar, please email the seminar coordinator.
ITEC 5900, Directed Studies
Directed Studies (DS) course allows students to work on a topic that is not offered as a regular course. Read the CSIT Graduate Manual for the procedure to follow before you can register. You must coordinate with a faculty member to submit a course outline and a Directed Study Registration form.
ITEC 5920 B, Special Topics in Digital Media
In this course we will look at how computer science is used throughout film production to improve quality, reduce costs, and bring viewers increasingly awe-inspiring experiences. Topics include: real-time previsualization, virtual cinematography, script analysis, box office forecasting, text-to-image generation, tracking and matchmoving, stereoscopic 3D, and security/watermarking.
Background: Students should be comfortable with:
- Programming in Python
- Statistics
- Algorithms
ITEC 5920 W, Selected Topics in Digital Media - Applied Deep Learning
Design, implement, and apply task-dependent deep learning architectures. Topics include mathematical overview of machine learning, deep feedforward networks, regularization for deep learning, optimization for training deep learning models, convolutional neural networks, recurrent neural networks, practical considerations and recent models and architectures in deep learning. Applications include signal processing and computer vision.
Background: As a graduate course, students should have the following skills:
- Calculus (BIT 1200), Linear Algebra (BIT 1201)
- Probability & Statistics (BIT 2000, BIT 2009)
- Ability to code in Python
Fast-track to PhD
The Fast-track option is to support students who are in a Master's program, have shown strong research potential, and intend to do a PhD. The Fast-track option allows students to enter the PhD program without finishing the Master’s requirements including the thesis. Discuss this option with your supervisor if you are doing a Master's.
The Fast-track applicants will be evaluated based on:
- Academic background: This includes GPA both at finished programs and any un-finished Master's.
- Master's courses are used as requirements for admission so in general do not count towards PhD requirements. But up to 1 credit of them can be used as Advanced Standing if approved by the School.
- Research Potential: In absence of a Master's thesis, applicants need to demonstrate sufficient research potential. This is similar to those with a no-thesis Master's degree. Examples of evidence includes but is not limited to:
- Peer-reviewed research publications.
- Research presentations at conferences and workshops.
- Research-based course projects with written deliverables.
- Typical minimum requirements are:
- GPA of 11.
- Completion of 2 semesters and 4 courses.
- Proof that a paper was submitted to a conference/workshop/journal.
- The Fast-track applications are reviewed by the Graduate Committee and if necessary approved by voting.
FGPA Fast-tracking Guidelines are available under Forms & Policies of their website: Fast-Tracking Guidelines