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# Charles Deledalle - Teaching

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- UCSD - Fall'17 - ECE285 Image and video restoration
- Miscellaneaous

## UCSD - Spring'18 - ECE285 Machine learning for image processing

#### Schedule

- Class: CSB-002. Mon/Wed/Fri 11-11:50am.
- Labs: EBU1-4307 Every two weeks starting April 9
- Group 1: Mon 2-4pm, Sneha Gupta (lastnames from A to Kh)
- Group 2: Wed 2-4pm, Charles Deledalle (lastnames from Ko to Ts)
- Group 3: Fri 3-5pm, Shobhit Trehan (lastnames from Tu to Zu)

- Office hours:
- Mon 2-4pm, Charles Deledalle (Room EBU1 4808, Jacobs Hall)
- Wed 3-5pm, Sneha Gupta, Shobhit Trehan (Room 4506, Jacobs Hall)

#### Lectures (login: ece, password: 285)

- Lecture #1 - slides: 1_intro.pdf (44Mb)
- Lecture #2 - slides: 2_predeep.pdf (13Mb)
- Lecture #3 - slides: 3_deep.pdf (16Mb)
- Lecture #4 - slides: 4_cnn.pdf (29Mb)
- Lecture #5 - slides: 5_detection.pdf (35Mb)
- Lecture #6 - slides: 6_generation.pdf (49Mb)

#### Assignments (login: ece, password: 285)

- Assignment #1 - instructions: lab1.pdf
- Assignment #2 - instructions: lab2.pdf
- Assignment #3 - instructions: lab3.pdf

#### Everything else

- Go to TritonEd

## UCSD - Fall'17 - ECE285 Image and video restoration

#### Schedule

- Class: EBU1-2315. Mon/Wed 11-12:20pm.
- Labs: EBU1-4309. Wed 2-4pm (starting Oct. 11).
- Office hours: EBU1-4808. Mon 2-4pm.

#### Lectures

- Lecture #1 - slides: 1_intro.pdf (61Mb) - handout: 1_intro-handout.pdf (33 pages, 60Mb)
- Lecture #2 - slides: 2_basics.pdf (25Mb) - handout: 2_basics-handout.pdf (31 pages, 26Mb)
- Lecture #3 - slides: 3_fourier.pdf (17Mb) - handout: 3_fourier-handout.pdf (28 pages, 18Mb)
- Lecture #4 - slides: 4_variational.pdf (36Mb) - handout: 4_variational-handout.pdf (33 pages, 35Mb)
- Lecture #5 - slides: 5_bayesian.pdf (58Mb) - handout: 5_bayesian-handout.pdf (39 pages, 56Mb)
- Lecture #6 - slides: 6_wavelets.pdf (93Mb) - handout: 6_wavelets-handout.pdf (44 pages, 67Mb)
- Lecture #7 - slides: 7_patches.pdf (34Mb) - handout: 7_patches-handout.pdf (20 pages, 34Mb)

- Important remarks:
- Printouts for the first lecture will be given to enrolled students. No other printouts will be given for the other lectures. Please, print the handouts yourself if you need a paper copy.

#### Homework

- Homework #1 - instructions: assignment1.pdf - data: assignment1.zip
- Homework #2 - instructions: assignment2.pdf - data: assignment2.zip
- Homework #3 - instructions: assignment3.pdf - data: assignment3.zip
- Homework #4 - instructions: assignment4.pdf - data: assignment4.zip
- Homework #5 - instructions: assignment5.pdf - data: assignment5.zip
- Homework #6 - instructions: assignment6.pdf - data: assignment6.zip
- Homework #7 - instructions: assignment7.pdf - data: assignment7.zip
- Homework #8 - instructions: assignment8.pdf - data: assignment8.zip
- Homework #9 - instructions: assignment9.pdf - data: assignment9.zip

- Important remarks:
- It is obvious that when you are asked to implement some functions, do not use implementations of others or the ones integrated to Matlab, such as:
`imfilter`

,`conv2`

,`medfilt2`

, ... - From this list, you are only allowed to use the ones under the section "Import, Export, and Conversion" and "Display and Exploration".

- It is obvious that when you are asked to implement some functions, do not use implementations of others or the ones integrated to Matlab, such as:

#### Project

- Deadline: December 12, 2017 (8pm)
- Subject here: project.pdf
- Data: project2.zip (23Mb), project3.zip (17Mb), project4.zip (885Mb), project5.zip (8.5Mb), project6.zip (14Mb).

## Miscellaneaous

- Cookbook for data scientists - cookbook_datascience.pdf

- Documentation for Git: https://www.atlassian.com/git/tutorials

Last modified: Fri Jun 1 19:19:41 Europe/Berlin 2018