Günter Rudolph: Lecture Introduction to Computational Intelligence (WS 2020/21)
| Dates: |
|
|
| Wednesday |
10:15 - 11:45 am |
online (via Zoom)
|
| First Lecture: |
Wednesday, 04-Nov-2020 |
News:
- Updated 25-Apr-2021, 04:00 pm:
Post-exam review: You are expected to receive a copy of the corrected exam and the sample solution by email on 28-Apr-2021.
There will be an online consultation on Friday, 07-May-2021, 1-2 pm,
where possible ambiguities in the assessment can be resolved.
You can get the zoom link from Moodle.
- Updated 29-Mar-2021, 12:00 pm:
Password: bC6PpjlgS95Nknsv
- Updated 22-Mar-2021, 05:00 pm:
Requirements for the exam are
HW: computer, internet, webcam, microphone.
SW: Adobe Reader (or the like). But you MUST be able to process form-based PDF.
You should test this with the test exam, Password: EasyPassword.
- Updated 05-Mar-2021, 11:00 am: (reminder)
The date for the 2nd written online-exam is Monday, 29-Mar-2021, 12:00 - 1:30pm. Registration deadline is 2 weeks prior to exam.
Registration via BOSS for degree programs Automation & Robotics, Data Science, Informatics, and Statistics;
exam participants of all other study programs should register by email to the professor.
Important: You cannot participate in the exam without due registration.
As usual, you may de-register up to one day prior to the exam.
- Updated 04-Mar-2021, 6:30 pm:
The results of the 1st exam can be retrieved from BOSS.
Performance record: download;
Grading System: download
- Updated 19-Feb-2021, 8:30 am:
The password can be found in Moodle.
- Updated 18-Feb-2021, 3 pm:
Last information (e.g. zoom link) and the encrypted PDF of the exam was sent today by email.
If you have not received the exam until 5 pm (today), please contact Prof. Rudolph by email
(but at first: check your SPAM folder!).
- Updated 10-Feb-2021, 1 am:
As announced in the lecture, we run a test exam today at 10:15 am.
All students who are registered to the exam on 19-Feb-2021 should have received an email with the test exam.
For your convenience, the encrypted test exam can be downloaded here.
The password will be published at 10:20 am: EasyPassword
The current version of the procedures regarding the exam on 19-Feb-2021 can be found here.
- Updated 02-Feb-2021, 4 pm:
The online exam will be realized by means of a form-based PDF file.
In order to work with this kind of file, it is necessary that the free Adobe Reader is running on your computer.
Otherwise, you cannot fill the form fields online!
Additionally, you need internet access, camera and microphone.
We shall use a Zoom session to verify your identity and to provide a channel to contact the supervisor.
The identity check will be done individually in a Zoom breakout room prior to the beginning of the exam.
After the identity check you may switch off camera and microphone.
The PDF file will be sent to you the day before the exam.
The exam is encrypted; the password will be published on the web page at the beginning of the exam.
When the exam time is over, you should store the file before you send it to a prescribed address by email.
We shall run a test of this procedure with all registered participants on Wednesday, 10-Feb-2021, 10:15am.
Thus, there will be no lecture at this date.
Important: You cannot participate in the exam without due registration.
The registration deadline is on Friday, 05-Feb-2021.
Registration must be made via BOSS (Computer Science, Automation and Robotics, Data Science, Statistics)
or by an email (all other study programs) sent to Prof. Rudolph.
As usual, you may de-register up to one day prior to the exam.
- Updated 28-Jan-2021, 1 pm:
The written exams in this semester will be online exams in "open book" format.
They will take place at the dates already published (see below).
You may register as of now. The eligibility of registration will be verified later.
The deadline for registration is 2 weeks prior to the exam, i.e., 05-Feb-2021 (mostly via BOSS).
De-registrations are possible up to 1 day (!) prior to exam.
It is not yet decided if the exam will use form-based PDF via email or the Moodle infrastructure.
The questions in the exam will be the same in both cases,
but the type of the questions will be slightly different to those in previous years
in order to avoid the necessity of writing mathematical formulae or creating graphics.
More details will follow shortly.
- Updated 31-Dec-2020, 12 pm:
Written exams: Friday 19-Feb-2021 and Monday 29-Mar-2021 (Corona permitting!).
Due to Corona pandemic it may happen that the exams are postponed to a currently undetermined date.
- Updated 23-Oct-2020, 11 pm:
The link to the online stream of the lecture is provided in the Moodle of the tutorial.
- Updated 11-Oct-2020, 6 pm:
Due to Corona pandemic the lecture will be presented in online mode.
You have to register for the lecture in order to be able to attend the online stream of the lecture.
Registration can be done in Moodle via the web pages of the tutorial (link below).
You will then receive further information via the email address you have specified there.
Attention: The access information is only sent via your unimail address!
If you do not have a unimail address (yet), register as a guest in Moodle and notify the instructor of the tutorial.
Tutorial: Marius Bommert (M.Sc.), LS 11
(web pages)
Instruction Language: English.
For a German version of this web page please click the 'Deutsch' button at the top right corner.
Description:
Typically, Computational Intelligence is used as an umbrella term for the fields of
artificial neural nets (ANN), fuzzy systems (FS) and evolutionary computation (EC).
This course offers a thorough introduction into all three fields.
Foundations of ANN: McCulloch-Pitts nets, perceptrons, RBF nets, Hopfield nets, convolutional neural nets (CNN), recurrent nets.
Foundations of FS: Fuzzy sets, fuzzy numbers, fuzzy logic, fuzzy reasoning.
Foundations of EC: Algorithmic basics, parameterization, analysis methods, limits of deployment.
Students should get an overview about the different aspects of Computational Intelligence.
They should be familiar with the essential elements in all three fields (ANN, FS, EC) and
able to deploy and adapt these methods for real applications. Moreover, they should be
able to assess when to deploy these methods and when not.
Slides:
|
Introduction (and Organizational Issues) |
|
 |
 |
 |
| 04.11.2020 |
Fuzzy Systems I |
|
|
|
|
| 11.11.2020 |
Fuzzy Systems II |
|
|
|
|
| 18.11.2020 |
Fuzzy Systems III |
|
|
|
|
(modified: slides 9, 10) |
| 25.11.2020 |
Fuzzy Systems IV |
|
|
|
|
| 02.12.2020 |
Evolutionary Algorithms I |
|
|
|
|
| 09.12.2020 |
Evolutionary Algorithms II |
|
|
|
|
| 16.12.2020 |
Evolutionary Algorithms III |
|
|
|
|
| 06.01.2021 |
Evolutionary Algorithms IV |
|
|
|
|
| 13.01.2021 |
Neural Networks I |
|
|
|
|
| 20.01.2021 |
Neural Networks II |
|
|
|
|
| 27.01.2021 |
Neural Networks III |
|
|
|
|
| 03.02.2021 |
Neural Networks IV |
|
|
|
|
|
| 10.02.2021 |
Exam Rehearsal |
|
| | |
|
Literature:
- A.E. Eiben and J.E. Smith: Introduction to Evolutionary Algorithms. Corrected 2nd printing. Springer 2007.
- Raul Rojas: Neural Networks - A Systematic Introduction. Springer 1996. Available online.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning. MIT Press 2017.
- G.J. Klir und B. Yuan: Fuzzy Sets and Fuzzy Logic. Prentice Hall 1995.
- F. Höppner, F. Klawonn, R. Kruse und T. Runkler: Fuzzy Cluster Analysis. Wiley 1999.
- Amit Konar: Computational Intelligence: Principles, Techniques and Applications. Springer 2005.
|
Home
ls11.cs.tu-dortmund.de>
