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| Integrated Lecture "Cognitive Algorithms" | == Integrated Lecture "Cognitive Algorithms" == |
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| We recommend the [[Main/WS19_MaschinellesLernen1 | "Machine Learning 1" ]] lecture or the [[Main/SS20_LabCourse| "Machine learning lab course" ]] for a more advanced treatment (this course is not a prerequisite). | |
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| TBD | === Dates === ||'''Lecture:''' ||Tuesday, 16:00-18:00 online || ||'''Tutorials:''' || TBD|| || '''ISIS '''||https://isis.tu-berlin.de/course/view.php?id=19753 || || '''Responsible:''' || [[http://www.ml.tu-berlin.de/menue/members/klaus-robert_mueller/|Prof. Dr. Klaus-Robert Müller]] || || '''Lecturer''' || Lorenz Vaitl || || '''Contact''' || cognitivealgorithms@ml.tu-berlin.de || === Lectures/Tutorials === Lectures and tutorials take place every other week respectively. There will be 3 groups for the tutorials, registration will happen via Moses. In the tutorials we will briefly recap the lecture and discuss the exercise sheet. === Prerequisites === The following prerequisites are helpful for taking the course: * Basic knowledge in linear algebra and calculus * Basic programming knowledge, programming in Python * To participate in the exam you '''first need to pass the elective course''' === Credits === The integrated lecture is the compulsory part of the B.Sc. module "Kognitive Algorithmen" in Computer Science. This "Kognitive Algorithmen" module is a 6 ECTS/SP module, and consists of * The compulsory integrated lecture (2 SWS / 3 SP / 3 ECTS), and * An elective (2 SWS / 3 SP / 3 ECTS): an [[Main/SS20_PyML| "Python for ML" ]] or a seminar: [[Main/SS19_HOT| "Hot Topics in ML"]] or [[ Main/SS20_KA_SE| "Cognitive Algorithms Seminar" ]] for a more in-depth treatment of selected applications. The grade will be determined in a written exam at the end of the semester. The grades of the elective will not count towards the grade of the entire module. |
Integrated Lecture "Cognitive Algorithms"
Computer programs can learn useful cognitive skills. This integrated lecture tries to communicate an intuitive understanding of elementary concepts in machine learning and their application on real data with a special focus on methods that are simple to implement. We recommend the "Machine Learning 1" lecture or the "Machine learning lab course" for a more advanced treatment (this course is not a prerequisite).
Dates
Lecture: |
Tuesday, 16:00-18:00 online |
Tutorials: |
TBD |
ISIS |
|
Responsible: |
|
Lecturer |
Lorenz Vaitl |
Contact |
Lectures/Tutorials
Lectures and tutorials take place every other week respectively. There will be 3 groups for the tutorials, registration will happen via Moses. In the tutorials we will briefly recap the lecture and discuss the exercise sheet.
Prerequisites
The following prerequisites are helpful for taking the course:
- Basic knowledge in linear algebra and calculus
- Basic programming knowledge, programming in Python
To participate in the exam you first need to pass the elective course
Credits
The integrated lecture is the compulsory part of the B.Sc. module "Kognitive Algorithmen" in Computer Science. This "Kognitive Algorithmen" module is a 6 ECTS/SP module, and consists of
- The compulsory integrated lecture (2 SWS / 3 SP / 3 ECTS), and
An elective (2 SWS / 3 SP / 3 ECTS): an "Python for ML" or a seminar: "Hot Topics in ML" or "Cognitive Algorithms Seminar" for a more in-depth treatment of selected applications.
The grade will be determined in a written exam at the end of the semester. The grades of the elective will not count towards the grade of the entire module.