Course Information

About

This course provides a comprehensive overview of fundamental topics in image processing for graduate students. The goal is to give a deeper understanding of the state-of-the-art methods in image processing literature and to study their connections. In this context, the course makes the students gain knowledge and skills in key topics and provides them the ability to employ them in their advanced-level studies. The course is taught by Erkut Erdem.

Instruction style: During the semester, students are responsible for studying and keeping up with the course material outside of class time. These may involve reading particular book chapters, or papers. After the first three lectures, each week a student will present a paper related to the topics of the week.

Time and Location

Lectures: Friday at 13:30-16:30 (Room D6)

Communication

The course webpage will be updated regularly throughout the semester with lecture notes, presentations, assignments and important deadlines. All other course related communications will be carried out through Piazza. Please enroll it by following the link https://piazza.com/hacettepe.edu.tr/spring2020/cmp717.

Pre-requisites

CMP717 is open to all graduate students in our CENG department. Prospective senior undergraduate students may sit in on the class. Non-CENG graduate students, however, should ask the course instructor for approval before the add/drop period. The prerequisites for this course are:

• Programming (you should be a proficient programmer to work out the practicals and to implement your course project.)
• Calculus (differentiation, chain rule) and Linear Algebra (vectors, matrices, eigenvalues/vectors)
• Basic Probability and Statistics (random variables, expectations, multivariate Gaussians, Bayes rule, conditional probabilities)
• Image Processing (you can still survive this course without an introductory level image processing course such as BBM413 Fundamentals of Image Processing before, but it is highly recommended.)
• Machine Learning (you can still survive this course without a machine learning course before, but it is highly recommended. Some introductory ML courses are BBM406 Fundamentals of Machine Learning and CMP712 Machine Learning.)
• Optimization (cost functions, taking gradients, regularization)

Grading

Grading for CMP717 will be based on

• Midterm Exam (24%),
• Practical (8%).
• Course Project (presentations and reports) (42%),
• Paper Presentations (14%) (6% overview, 4% pros, and 4% cons),
• Paper Summaries (12%).