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.  

Summary
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 2024/07/15
Cost CHF 9'600
Special Offer Students and Employees of the University of Bern: CHF 5’600
Organising institutions Mathematical Institute
Registration

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

 

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.

Building Abbreviations
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. 

Introduction Events 2023
Date Time Location Title Lecturer Comments
2023-03-27 18:15 - 20:00 HG 212 Introduction to CAS AML PD Dr. S. Haug Link to Zoom Meeting.
2023-06-19 18:15 - 20:00 HG 212 Introduction to CAS AML PD Dr. S. Haug Link to Zoom Meeting. Slides.

Introductionary courses

Prepare yourself for the CAS Modules. We offer the following introductionary courses to refresh your knowledge.

Data Science Fundamentals 2023
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.

CAS AML Modules 2023/2024
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 August 30 Please find information here.
Building Abbreviations
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. 

Introduction Events 2024
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
2024-06-12 18:15 - 20:00 HG 208 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.

Data Science Fundamentals 2024
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 course will appear here.

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 course will appear here.

Modules

All Information about Modules 1-6. 

Course materials are accessed via the Ilias Learning Platform. The Link to the course content will appear here. 

CAS AML Modules 2024/2025
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, 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
2024-10-18 until 2024-12-13
13:15 - 15:00 ExWi, room B001
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
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

Informal events, other 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!

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 (chair)
  • Prof. Dr. Thomas Wihler

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. 

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.

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

 

Anonymous

"I can better support customers in Machine Learning projects and have become more efficient in the implementation."

Chris Kopp

PostNetz

"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."

Success Story

Click here to learn more about a case project that was developed by two of our participants as part of the CAS.

Contact

Cheyenne Friedrich

DI & MI

cheyenne.friedrich@unibe.ch

Assistant Education and Communication Manager Continuing Education in Extended Intelligence

Previous

Associate Courses

CAS Natural Language Processing - AI for Language

Degree CAS
Start 08/2024
Language Englisch
Cost CHF 9'600

The interest in Natural Language Processing (NLP) and its AI applications has increased massively in recent years. NLP belongs to both computational linguistics as its engineering domain and artificial intelligence as an increasingly important subdomain. The applications based on deep neural networks have reached a performance level which cannot be ignored by any field that is processing natural languages, see Large Language Models like BERT, ChatGPT, Gemini etc.

Mathematical Institute