MAI 645: Machine Learning for Graphics and Computer Vision

Instructor: Andreas Aristidou
Type: Postgraduate (Elective)
Prerequisite: Knowledge of a high-level programing language, and experience in programming with Python. Experience with linear algebra, calculus, statistics, and probability.
Lectures: Thursday, 15:00-18:00 (ΘΕΕ01 #202)
Recitations: Thursday, 14:00-15:00 (ΘΕΕ01 #202)
Laboratory: Wednesday, 18:00-19:30 (ΘΕΕ01 #101)
Teaching Assistants: Yiangos Georgiou


This course will offer an introduction to machine learning algorithms, the use of deep learning and its applications in computer vision and graphics. The course will also operate as a graduate-level seminar with weekly readings (1 hour per week), summarizations, and discussions of recent papers.
You can download the syllabus of the course here...


Sign-up now to Moodle using code handed out in class!

Course Schedule and Lectures

  1. Introduction to Deep Learning in Graphics and Computer Vision Course Objectives and Syllabus.
    [PDF in EN | 10.29 MB]
  2. Image Classification Introduction to image classification, supervised/unsupervised methods, linear classifiers.
    [PDF in EN | 19.02 MB]

Lab Schedule


All Assignments will be announced in Moodle. Sign-up using the code handed out in class!

Text Book and Bibliography

© 2017 Andreas Aristidou