CAS in Advanced Machine Learning
Mathematical Institute
In many disciplines, the amount of available data and the computing capacity are growing rapidly. This enables the application of machine learning methods on tasks previously being reserved for humans. The format is designed to align with the participants’ main study and or professional activities. The teaching and learning approaches are team and discussion oriented and designed to develop practical competency. It is structured in six modules which are offered in blocks and Fridays' afternoon. The CAS is at a university master level and programming and some machine learning skills from eduction or profession are required, e.g. the CAS Applied Data Science.
Degree | Certificate of Advanced Studies in Advanced Machine Learning AML University of Bern (CAS AML Unibe) |
---|---|
Start | 08/2024 |
Length | August 2024 - July 2025 |
Scope | 16 ECTS |
Cycle | Annual |
Flexible entry possible | No |
Single module visitable | Yes |
Place | University of Bern; Mürren, Bernese Oberland (Module 6); Giens peninsula, southern France (Module 3) |
Language | English |
Admission | See tab "Admission" |
Registration until | 2024/08/04 |
Cost | CHF 9'600 |
Special Offer | Students and Employees of the University of Bern: CHF 5’600 |
Organising institutions | Mathematical Institute |
About the program
In many disciplines, the amount of available data and the computing capacity are growing rapidly. This enables the application of machine learning methods on tasks previously being reserved for humans. Trained machines outperform homo sapiens in more and more cognitive tasks. As with other disruptive technology emergences, the resulting automation potential represents a huge benefit for the human society, but also comes with new challenges and risks. This CAS offers the opportunity to complement your data science competences with a formal deepening and broadening of knowledge and skills on machine learning and artificial intelligence. The format is designed to align with the participants main study and or professional activities. The teaching and learning approaches are team and discussion oriented and designed to develop practical competency.
The program is organised into six modules, running over 18 course days from August to January and targets professionals and researchers in the private and public sector. The content covers a review of machine learning methods, established applications, the research frontier and philosophical and ethical aspects. The difficulty is at a university master level and assumes own basic machine learning experience, programming skills and a higher education degree with some mathematical background. The program focuses on concepts and usage of common machine learning tools, not so much on theoretical elaboration of the mathematics, statistics and informatics.
Objectives
Course competence is developed throughout six modules and a CAS project work. On completion the graduates will (be able to):
- design, tune, train and measure performance of neural networks with advanced deep learning libraries
- understand the inner mechanisms of neural networks during training
- familiar with active research in machine learning
- understand and communicate scientific publications on machine learning and artificial intelligence
- familiar with the philosophy and ethics of extended andartificial intelligence
- familiar with new and advanced machine learning models
Modules
If there are free places, modules can be attended individually.
Module 1: Review of machine learning, practical methodology and applications
Block module. Review of basic principles, concepts, practical methodology and applications of machine learning.
Module 2: Deep networks
Block module. Study of established deep network applications.
Module 3: Advanced Models I
This year we expect to do time-series and autoencoders. Module 3 traditionally takes place at the mediterranean coast. Full pension hotel accommodation is included in the CAS fee.
Module 4: Selected topics on machine learning
Participants study selected publications on machine learning and artificial intelligence and present them to the others.
Module 5: Philosophy and Ethics of extended cognition and artificial intelligence
Artificial Intelligence as a scientific field dates back to the 1950s. This module concerns key philosophical and ethical questions and discussions triggered by the existence of intelligence outside the human brain.
Module 6: Advanced Models II
This year we expect to do Transformers and Reinforcment Learning. Module 6 traditionally takes place in the wonderful historic hotel Regina in the ski resort Mürren (Bernese oberland), only about two train hours from Bern. Full pension hotel accommodations are included in the CAS fee.
All modules
The duration of all modules corresponds to approximately 20 classroom hours each and module work (expected eff ort is 30 hours), with each complete module qualifying for 2 ECTS points. The expected workload for the CAS Project is 120 hours.Main tools and CAS language are Python, TensorFlow and Git. Other tools may be used, then with limited support. Computational resources are offered.
Schedule 2023/24
Abbreviation | Building |
---|---|
ExWi | Exakte Wissenschaften (Sidlerstrasse 5) |
UniM | Uni Mittelstrasse (Mittelstrasse 43) |
HG | Main Building (Hochschulstrasse 4) |
PT | Parkterrasse 14 |
UT | Unitobler (Muesmattstrasse) |
Information Events
Learn everything you need to know about the CAS in Advanced Machine Learning. One introduction is mandatory, remote participation is possible.
Introductionary courses
Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.
Title | Date | Time | Location | Lecturer | Description | Comments |
---|---|---|---|---|---|---|
Mathematical Methods for Data Science and Machine Learning |
2023-08-15 - 2023-08-18 (4 half days) |
09:15 - 12:30 | UT
F-121 |
Dr. K. Sipos | This course is recommended for CAS AML students who wish to deepen their mathematical knowledge or who are new to machine learning mathematics. | Link to Ilias course |
Introduction to Programming (Python) |
2023-08-14 | 09:15 - 17:00 | UT
F-123 |
Dr. K. Sipos | This course is intended for CAS ADS students but CAS AML participants who would like to refresh their Python programming knowledge are welcome too. | Link to Ilias course |
Modules
All Information about Modules 1-6.
Course materials are accessed via the Ilias learning platform.
Module | Course title | Date | Time | Location | Lecturer(s) | Comments |
---|---|---|---|---|---|---|
Module 1 |
M1 Review of Machine Learning, Practical Methodology and Applications | 2023-08-22 - 2023-08-25 (4 half days, afternoons for self-study) |
09:00 - 12:30 | UT
F-107 |
Dr. G. Schaller Conti | |
M1 Project | TBD | |||||
Module 2 |
M2 Deep Networks | 2023-08-29 - 2023-09-01 (4 half days, afternoons for self-study) |
09:00 - 12:30 | UniM
120 |
Dr. G. Schaller Conti | |
M2 Project | 2023-09-29 | 08:15 - 14:00 | HG 117 |
Zoom Link | ||
Module 3 |
M3 Advanced Models I (Time-Series and Autoencoders) | 2023-10-02 - 2023-10- 06 (5 Days) |
08:30 - 12:30 17:00 - 19:00 |
Combo Venice | Dr. M. Vladymyrov, Dr. L. Brigato, PD Dr. S. Haug et al. | Monday is arrival day - On Monday, the module content starts 17:00. On Friday the module ends at 12:30 |
M3 Project | TBD | |||||
Module 4 |
M4 Selected Topics on Machine Learning and Artificial Intelligence | Every Friday 2023-10-20 until 2023-12-15 |
13:15 - 15:00 | ExWi B77 |
Dr. M. Vladymyrov, PD Dr. S. Haug, Dr. L. Brigato | |
M4 Project | TBD | |||||
Module 5 |
M5 Philosophy and Ethics of Extended Cognition and Artificial Intelligence | Every Friday 2023-10-20 until 2023-12-15 |
15:15 - 17:00 | ExWi B77 |
Prof. Dr. Dr. C. Beisbart | |
M5 Project | TBD | |||||
Module 6 |
M6 Advanced Models 2 (Transformers and Reinforcement Learning) | 2024-01-22 - 2024-01-26 (5 Days) |
08:30 - 12:30 17:00 - 19:00 |
Hotel Regina Mürren (Bernese Oberland) |
Dr. M. Vladymyrov, Dr. L. Brigato, PD Dr. S Haug et al. | On Friday the module ends at 12:30 |
M6 Project | TBD | |||||
Final Project |
Deadline | June 15 | Please find information and submission here. | |||
Graduation Event | Poster Session Graduation Party |
2024 August 30 | 14:30 17:00 |
ExWi Foyer, LesBar (Münstergasse 63, 3011 Bern) |
Deadline registration and poster session: 25.08.2024 Please find more information here. |
Schedule 2024/25
Abbreviation | Building |
---|---|
ExWi | Exakte Wissenschaften (Sidlerstrasse 5) |
UniM | Uni Mittelstrasse (Mittelstrasse 43) |
HG | Main Building (Hochschulstrasse 4) |
PT | Parkterrasse 14 |
UT | Unitobler (Muesmattstrasse) |
Information Events
Learn everything you need to know about the CAS in Advanced Machine Learning. One introduction is mandatory, remote participation is possible.
Date | Time | Location | Title | Lecturer | Comments |
---|---|---|---|---|---|
2024-03-11 | 18:15 - 20:00 | HG 208 | Introduction to CAS AML | PD Dr. S. Haug | Link to Zoom Meeting |
2024-07-10 | 18:15 - 20:00 | UniS Raum S 201 | Introduction to CAS AML | PD Dr. S. Haug | Link to Zoom Meeting |
Introductionary courses
Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.
Title | Date | Time | Location | Lecturer | Description | Comments |
---|---|---|---|---|---|---|
Mathematical Methods for Data Science and Machine Learning |
2024-08-13 - 2024-08-16 (4 half days) |
09:15 - 12:30 | UT, room F -121 | Dr. K. Sipos | This course is recommended for CAS AML students who wish to deepen their mathematical knowledge or who are new to machine learning mathematics. | Link to Ilias |
Introduction to Programming (Python) |
2024-08-12 | 09:15 - 17:00 | UT, room F -121 | Dr. K. Sipos | This course is intended for CAS ADS students but CAS AML participants who would like to refresh their Python programming knowledge are welcome too. | Link to Ilias |
Modules
All Information about Modules 1-6.
Course materials are accessed via the Ilias Learning Platform.
Module | Course title | Date | Time | Location | Lecturer(s) | Comments |
---|---|---|---|---|---|---|
Module 1 |
M1 Review of Machine Learning, Practical Methodology and Applications | 2024-08-20 - 2023-08-23 (4 half days, afternoons for self-study) |
09:00 - 12:30 | UniM, room 324 | Dr. G. Schaller Conti | |
M1 Project Deadline | TBD | |||||
Module 2 |
M2 Deep Networks | 2024-08-27 - 2024-08-30 (4 half days, afternoons for self-study) |
09:00 - 12:30 | Tuesday, 2024-08-27: UniM, room 220 Wednesday, 2024-08-28: UniM, room 220 Thursday, 2024-08-29: UniM, room 320 Friday, 2024-08-30: UniM, room 124 |
Dr. G. Schaller Conti | |
M2 Project Deadline | TBD | |||||
Module 3 |
M3 Advanced Models I (Time-Series and Autoencoders) | 2024-09-30 - 2024-10-04 (4 Days) |
08:30 - 12:30 17:00 - 19:00 |
Giens Peninsula near Hyères (southern France) | Dr. M. Vladymyrov, PD Dr. S. Haug | Monday is arrival day - On Monday, the module content starts 17:00. On Friday the module ends at 12:30 |
M3 Project | TBD | |||||
Module 4 |
M4 Selected Topics on Machine Learning and Artificial Intelligence | Every Friday 2024-10-18 until 2024-12-13 |
13:15 - 15:00 | ExWi, room B001 |
Dr. M. Vladymyrov, PD Dr. S. Haug | |
M4 Project | TBD | |||||
Module 5 |
M5 Philosophy and Ethics of Extended Cognition and Artificial Intelligence | Every Friday 2024-10-18 until 2024-12-13 |
15:15 - 17:00 | ExWi, room B001 | Prof. Dr. Dr. C. Beisbart | |
M5 Project | TBD | |||||
Module 6 |
M6 Advanced Models 2 (Transformers and Reinforcement Learning) | 2025-01-20 - 2025-01-24 (4 Days) |
08:30 - 12:30 17:00 - 19:00 |
Hotel Regina Mürren (Bernese Oberland) |
Dr. M. Vladymyrov, Dr. L. Brigato, PD Dr. S Haug et al. | Monday is arrival day - On Friday the module ends at 12:30 |
M6 Project | TBD | |||||
Final Project |
||||||
Final Project deadline | TBD | |||||
Graduation Event | End August |
Events and other important dates
Title | Date | Time | Location | Comments |
---|---|---|---|---|
CAS Apero |
2024-08-23 | 17:00 | TBD | Come together and have a drink or two |
CAS Completion Notification |
2025-07-31 | Be informed that you have completed the CAS programme | ||
CAS Graduation Celebration |
late August / early September | TBD | TBD | Celebrate your Graduation! |
Organising institution and faculty
The Certificate of Advanced Studies (CAS) in Advanced Machine Learning (AML) is offered by the Mathematical Institute.
Program management
- Prof. Dr. Jan Draisma
- Prof. Dr. Tobias Hodel
- Prof. Dr. Paolo Favaro
- PD Dr. Sigve Haug (director of studies)
- Prof. Dr. Christiane Tretter
- Prof. Dr. Thomas Wihler (chair)
Lecturers
Lecturers include
- Prof. Dr. Dr. Claus Beisbart
- PD Dr. Sigve Haug
- Dr. Kinga Sipos
- M.Sc. Pablo Verges
- Dr. Mykhailo Vladimirov
- Dr. Guillame Witz
- Dr. Geraldine Schaller Conti
- et al.
Admission
The CAS is at a university master level and programming and some machine learning skills from eduction or profession are required, e.g. the CAS Applied Data Science.
Target groups
Aimed at students and professionals from the public and private sector that hold a degree from a university or a university of applied sciences (e.g. BSc, MSc, PhD).
SUITABLE FOR MANAGEMENT ► wanting to know how machine learning is performed, limitations and possibilities, ethical aspects
RELEVANT FOR DATA ANALYSTS ► wanting to deepen and update their machine learning skills
APPLICABLE TO CONSULTANTS ► with a desire to know and exploit the possibilities offered by machine learning methods
INTENDED FOR RESEARCHERS ► wanting to extend the machine learning application in their field
Standard data sets are provided, but participants are encouraged to bring or acquire their own. If you have any questions regarding whether this program could work for you, please do not hesitate to contact us.
Admission requirements
- A university degree and basic knowledge of mathematics, statistics, programming, machine learning and professional or research experience in the field of data analysis are required for admission to the course. The required basic knowledge is based on the level of an introductory lecture as part of an undergraduate master’s degree. The program management specifies these requirements.
- Exceptions to the admission requirements can be approved by the program management “sur Dossier”. For people without a university degree, they can impose additional requirements for admission to ensure that they can successfully complete the course.
- Interested parties who only want to take part in individual modules can be admitted, provided that there are free course places.
The program management decides on admission to the course. There is no entitlement to admission.
Registration opens in November and a maximum of 20 registrations can be accepted each year. Registrations are processed in the order of arrival. The CAS can only be offered if there are sufficient registrations by the deadline.
Deadline: 2025-06-30
Application and tuition fees
Program fees
Regular CAS program: CHF 9'600.-
Students currently enrolled in an University or University of Applied Sciences: CHF 5'600.-
Employees of University of Bern: CHF 5'600.-
lnclusive of all modules, performance assessments, certificates, materials & teaching platforms, coffee breaks, full pension hotels (Module 3 and Module 6) and diploma apero.
If there are free places, modules can be attended individually. Prices are CHF 300.- per half day. Individual modules are accredited with certificates which are accumulated for the full CAS AML
Project works / Testimonials
Random examples of what CAS participants say:
"I can better support customers in Machine Learning projects and have become more efficient in the implementation."
"The applied work after each module meant lots of coding and some swearing, but the good kind, where you are finally satisfied by what you achieved."
"I would like to thank Sigve Haug for organizing and leading the very insightful course “Certificate of Advanced Studies in Advanced Machine
Learning AML."
”
Contact
Claire Dové
DI & DO
Sigve Haug
MO-FR
Cheyenne Friedrich
DI & MI
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