CENG 483 Introduction to Computer Vision (Fall 2021)

Instructor:	Gokberk Cinbis
TA:	Bugra Baran
Syllabus:	Please read the syllabus for course details.
Important note:	Lectures start at 8:50am on Tue and Wed, unless announced otherwise.

Tentative weekly schedule

Week	Lectures	Optional readings
	Lecture: Introduction, course logistics
	Lecture: Linear algebra review	NumPy tutorial by Ranjay Krishna.
	Lecture: Color Image formation	The CIE XYZ and xyY Color Spaces by Douglas A. Kerr Paper is white because it reflects all light, and mirrors reflect all light, so why don't they look the same?
	Lecture: Filtering Edge detection	Sections 3.1 and 3.2 of Richard Szelski's book.
	Lecture: Interest point detection, corner detection
	Lecture: Scale selection, local descriptors, SIFT	Histograms of Oriented Gradients for Human Detection.
	Lecture: Clustering Bag of visual words
	Lecture: Introduction to learning-based vision	ML Basics (Deep Learning book by Goodfellow et al.)
	Lecture: Loss functions Midterm: 14 December 2021, 18:00-19:40 (2 sessions, 40 min each), classrooms: BMB4, BMB5.	Linear classification (from CS231n)
	Lecture: Optimization	Optimization-1 (from CS231n) Optimization-2 (from CS231n)
	Lecture: Deep network basics Deep network training fundamentals
	Lecture: Convolutional neural networks	Neural networks-1 (from CS231n) Neural networks-2 (from CS231n) Neural networks-3 (from CS231n)
	Lecture: Example ConvNet architectures	Case study (from CS231n)
	Lecture: Sensing depth: stereo vision, monocular depth cues, structure from motion	ConvNets (from CS231n)

All assignments are announced on ODTUClass.