[credit: http://danjodon.deviantart.com/] 
This course introduces the fundamentals of designing computer vision systems: that can "look at" images and videos and reason about the physical objects and scenes they represent. We will learn about methods for image restoration and enhancement; for estimating color, shape, geometry, and motion from images; and for image segmentation, recognition, and classification. The focus of the course will be on the mathematical tools and intuition underlying these methods: models for the physics and geometry of image formation, and statistical and machine learningbased techniques for inference. 
No prior knowledge of computer vision, image processing, or graphics is required. The following skills are necessary for the class:
Additionally, prior experience with signal processing and machine learning will be helpful (but isn't necessary).
Late Policy: All problem sets are due by 11:59 pm of the due date (although we don't suggest waiting till then to submit). You get three "late days" total for the course. Beyond that, you will lose 25% for each day a submission is late (this is quantized to days: anything that is submitted between 1 minute and 24 hours after the due date will be penalized 25%). Late penalties will not be reflected in the initial grade for each set, but will be applied at the end of the term after factoring in the late days (in a way that maximizes your points).
Collaboration Policy: All problem sets, and the final project, are expected to be completed individually. All code and written work you submit must be your own. Discussion about course topics with your classmates is encouraged (in person, and on the course discussion forum), but remember:
Follow both the letter and spirit of this policy. The problem sets account for most of your grade, and it is important we be able to evaluate how well you, personally, have understood the course material. Discussions with classmates should be to gain a better understanding of the course material in general, not the specific problems in the problem sets. If you are stuck on a problem, ask for help from the course staff instead of from your classmates (or online). If a classmate asks for help and you can't be sure you can help them without "revealing" a crucial part of the answer, ask them to contact us.
There are no required textbooks for the course. The following are useful for reference:
Week  Tuesday  Thursday 

1 
Aug 29
Introduction. Cameras & Image Formation I. 
Aug 31
Image Formation II. Perpixel Image Ops. Convolutions I. 
2 
Sep 5
Convolutions II. Edge & line detection. Other spatial image ops. 
Sep 7
Fourier Transforms & Convolution Theorem. 
3 
Sep 12 PS1 Out
Scale & Multiscale Representations. Efficient Convolutions. Image Restoration. 
Sep 14 
4  Sep 19 
Sep 21
Material & Shading: Lambertian, specular, BRDFs. 
5 
Sep 26 PS1 Due! PS2 Out.

Sep 28
Photometric Reasoning Roundup. Camera Projection & Geometry. 
6  Oct 3  Oct 5 
7 
Oct 10 PS2 Due! PS3 Out.

Oct 12 
8 
Oct 17
Fall Break: No class. 
Oct 19 
9 
Oct 24

Oct 26 PS3 Due!

10 
Oct 31 Project Proposals Due! PS4 Out.
Optical Flow. Segmentation & Grouping. 
Nov 2

11  Nov 7 
Nov 9
Logistic Regression, Gradient Descent & SGD/Momentum. 
12 
Nov 14 PS4 Due! PS5 Out.

Nov 16
Autograd. Multilabel Classification. Convolutional Layers. 
13 
Nov 21
Network Architectures. Batchnorm & Dropout. 
Nov 23
Thanksgiving Break: No class. 
14 
Nov 28
Generative Adversarial Networks. 
Nov 30 PS5 Due on 12/1!
Recent advances. 
15 
Dec 5
Project Presentations. 
Dec 7 Project Final Reports Due.
Project Presentations. 
The slides, syllabus, and problem sets are based on excellent computer vision courses taught elsewhere by Todd Zickler, Bill Freeman, Svetlana Lazebnik, James Hays, Alyosha Efros, Subhransu Maji, and many many others.