Connectionists: Asynchronous access to Computer Vision and Machine Learning (CVML) Web lecture repository

[ioannakoroni at csd.auth.gr]

Dear Computer Vision/Machine Learning/Autonomous Systems students,
engineers, scientists and enthusiasts,

 

due to numerous requests worldwide, asynchronous access is now offered to
Computer Vision and Machine Learning (CVML) Web lecture material:

a) lecture pdf/ppt, b) lecture video, c) lecture understanding questionnaire
d) lecture satisfaction questionnaire. 

This allows individual study at own pace.

 

The following lectures are already in the repository:

*	Introduction to Autonomous Systems
*	Introduction to Computer vision
*	Image acquisition, camera geometry   
*	Stereo and Multiview imaging 

Two more lectures are added each week, the next to come being:

*	Structure from Motion  
*	2D convolution and correlation algorithms

 

Registration  can be done using the link:
http://icarus.csd.auth.gr/cvml-web-lecture-series/

No new registration is needed for old registrants, just an email to Ioanna
Koroni <koroniioanna at csd.auth.gr <mailto:koroniioanna at csd.auth.gr> >  or
Orestis Sarakatsanos orestiss at csd.auth.gr <mailto:orestiss at csd.auth.gr> .

All other provisions for CVML Web lectures (certificate of participation
etc) apply.

 

These  lectures are part of a 14 lecture CVML web course 'Computer vision
and machine learning for autonomous systems' (April-June 2020):

 

Introduction to autonomous systems
(delivered 25th April 2020)

Introduction to computer vision
(delivered 25th April 2020)

Image acquisition, camera geometry
(delivered   2nd May 2020)

Stereo and Multiview imaging
(delivered   2nd May 2020)

Structure from Motion  

2D convolution and correlation algorithms

Motion estimation 

Introduction to Machine Learning

Introduction to neural networks, Perceptron, backpropagation

Deep neural networks, Convolutional NNs

Deep learning for object/target detection

Object tracking 

Localization and mapping

Fast convolution algorithms. CVML programming tools.

 

Lecturer: Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer,
EURASIP fellow) received the Diploma and PhD degree in Electrical
Engineering, both from the Aristotle University of Thessaloniki, Greece.
Since 1994, he has been a Professor at the Department of Informatics of the
same University. He served as a Visiting Professor at several Universities.

His current interests are in the areas of image/video processing, machine
learning, computer vision, intelligent digital media, human centered
interfaces, affective computing, 3D imaging and biomedical imaging. He has
published over 1138 papers, contributed in 50 books in his areas of interest
and edited or (co-)authored another 11 books. He has also been member of the
program committee of many scientific conferences and workshops. In the past
he served as Associate Editor or co-Editor of 9 international journals and
General or Technical Chair of 4 international conferences. He participated
in 70 R&D projects, primarily funded by the European Union and is/was
principal investigator/researcher in 42 such projects. He has 30000+
citations to his work and h-index 81+ (Google Scholar). 

Prof. Pitas lead the big European H2020 R&D project MULTIDRONE:
https://multidrone.eu/ and is principal investigator (AUTH)  in H2020
projects Aerial Core and AI4Media. He is chair of the Autonomous Systems
initiative https://ieeeasi.signalprocessingsociety.org/.

Prof. I. Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ
<https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el> &hl=el

AIIA Lab www.aiia.csd.auth.gr <http://www.aiia.csd.auth.gr> 

Lectures will consist primarily of live lecture streaming and PPT slides.
Attendees (registrants) need no special computer equipment for attending the
lecture. They will receive the lecture PDF before each lecture and will have
the ability to ask questions real-time. Audience should have basic
University-level undergraduate knowledge of any science or engineering
department (calculus, probabilities, programming, that are typical e.g., in
any ECE, CS, EE undergraduate program).  More advanced  knowledge (signals
and systems, optimization theory, machine learning) is very helpful but nor
required.

 

Sincerely yours

Prof. Ioannis Pitas

Director of AIIA Lab, Aristotle University of Thessaloniki, Greece

 

Post scriptum: To stay current on CVMl matters, you may want to register to
the CVML email list, following instructions in
https://lists.auth.gr/sympa/info/cvml

 

 

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