Probabilistic Models of the Visual Cortex:

`Tues/Thurs: 9:00-10:15am, Krieger 205, Fall 2021.`

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The course gives an introduction to computational models of the mammalian visual cortex. It covers topics in low-, mid-, and high-level vision. It briefly discusses the relevant evidence from anatomy, electrophysiology, imaging (e.g., fMRI), and psychophysics. It concentrates on mathematical modelling of these phenomena taking into account recent progress in probabilistic models of computer vision and developments in machine learning.

- Instructor: Prof. Alan Yuille, ayuille1@jhu.edu, office hours: by email appointment. TA: Fengze Liu, fliu23@jhu.edu, office hours Friday 4-5pm, CA: Yihong Sun, ysun86@jhu.edu, office hours Monday 3-4pm
- Piazza discussion site [Piazza Course Site] We encourage the students to post questions about the course and homeworks on Piazza.

- Course notes and course
readings (posted online).

- A.L. Yuille and D.K. Kersten. EarlyVision. Invited Book
Chapter in
**From Neuron to Cognition**. 2016.*This covers much of the first part of the course.*

**Understanding Vision:**Theories, Models, and Data. Zhaoping Li. Oxford University Press. 2014.*This**gives more detailed coverage of some topics in the first part of the course.*

- Note:
*the second part of the course is based on recent work and so there is no book reference (just class notes and readings).* **Seeing:**The Computational Approach to Biological Vision. J.P. Frisby and J.V. Stone. (2nd Ed) MIT Press. 2010.*Introduces the computational approach to vision but with limited mathematics.***Stanford tutorials**, the engineering website, http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial, is a good resource for students with limited statistics background, as it introduces very basic regression, dimension reduction (PCA, ICA) and vectors.**Stanford tutorials,**http://vision.stanford.edu/teaching/cs231n/2017/ is a good supplement for the later part of the course, it describes how convolutional network are done and has a lot of codes and examples.- Syllabus.

Grading Plan: 5 homework assignments will be posted here(roughly biweekly). The assignments need to be submitted via Gradescope (Entry Code: N8JYDJ).

Homework 1 Due on Oct 5 before class.

Homework 2 Due on Oct 26 before class.

Homework 3 Due on Nov 16 before class.

Homework 4 Due on Dec 14 11:59pm.

Homework 5 Optional to submit.