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 08/2023
Length August 2023 - July 2024
Scope 16 ECTS
Cycle Annual
Flexible entry possible No
Single module visitable Yes
Place University of Bern; Mürren, Bernese Oberland (Module 6); Mediterranean coast (Module 3)
Due to the pandemic situation and the related official requirements, we would like to point out that the modules are always accessible online.
Language English
Admission See tab "Admission"
Registration until 2023/06/01
Cost CHF 9'600
Special Offer Employees and Students 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 one or more applied machine learning domains, the main mathematical methods for data science and machine learning or basic entrepreneurship (elective module) 

 

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 commonly used in industry.

Module 3: Deep learning research 

Study of new promising, but not yet widely established approaches with deep networks.

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: Elective module 

One 2 ECTS  module on machine learning in an applied domain or mathematical methods for machine learning and data science. Elective modules might differ from year to year (see Schedule).

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.

Success Story

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

Rooms and lecturers to be confirmed. All courses take place in walking distance from the Bern railway station. The exception is Module 3 which takes place on Mallorca (stay and full board included in the fee).

Building abbreviations
Abbreviation Building 
ExWi Exakte Wissenschaften (Sidlerstrasse 5) 
UniM Uni Mittelstrasse (Mittelstrasse 43) 
PT Parkterrasse 14
HG Main Building (Hochschulstrasse 4) 
UniS UniS (Schanzeneckstrasse 1) 
VR VonRoll (Fabrikstrasse )
CAS AML Schedule 2022/23 (repeated annually)
Date Time   Room  Module  Lecturer  Comment

2022-06-13

18:00-19:00 Online Introduction to the CAS AML PD. Dr. S. Haug  One introduction is mandatory. No registration needed. Join online here.
           

2022-08-16 –
2022-08-19

09:00-12:30 UniM 324 M6 Mathematical Methods Dr. K. Sipos

Elective Module 6

Link to Ilias course

           

2022-08-23
2022-08-24
2022-08-25
2022-08-26

09:00-12:30 HG 105  M1 Review of Machine Learning, Practical Methodology and Applications    Dr. G. Conti Link to Ilias course. On Friday apero at 5 pm.
           

2022-08-30
2022-08-31
2022-09-01
2022-09-02

09:00-12:30

HG 105

M2 Deep Networks

Dr. G. Conti Link to Ilias course. On Friday graduation event at 5 pm.

2022-10-04
2022-10-05
2022-10-06
2022-10-07

08:30-12:30 17:00-19:00 Mallorca M3 Deep Learning Research (autoencoders and reinforcement learning)
Dr. Mykhailo Vladymyrov, Dr. Lorenzo Brigato, PD Dr. Sigve Haug et al.
Monday apero at 17:00. On Friday the school ends at 12:30. Link to course.
           

2022-10-21
2022-10-28
2022-11-04
2022-11-11
2022-11-18
2022-11-25
2022-12-02
2022-12-09
2022-12-16

13:15-15:00

PT 323

M4 Selected Topics on Machine Learning and Artificial Intelligence (seminar), not on Nov 11. Dr. Vladymyrov, PD Dr. Haug, Dr. Brigato

Friday afternoon courses

Link to Ilias course 

2022-11-10 15:15-18:00 HG 331 M5 Machine Learning Limitations: Shortcuts, Errors and Adversarial Attacks Prof. Dr. Felix Wichmann Link to Ilias course.

2022-10-21
2022-10-28
2022-11-04
2022-11-11
2022-11-18
2022-11-25
2022-12-02
2022-12-09
2022-12-16

15:15-17:00

PT 323

M5 Philosophy and Ethics of Extended Cognition and Artificial Intelligence Prof. Dr. Dr. C. Beisbart

Friday afternoon courses

Link to Ilias course 

           

(2023-01-16)

2023-01-17
2023-01-18
2023-01-19
2023-01-20

09:00 - 12:30   Bern, HG 114 (NLP), 115 (MLCV), 117/104 (MLTS) M6 (one elective module is needed, choose from the links to the right. The Mathematical Methods Module from August also counts)  

Link to Machine Learning for Computer Vision 

Link to Natural Language Processing (NLP) 

Link to Machine Learning for Time Series

           

2023-04-XX
2023-05-XX

    Project discussions   We will find a couple of dates for discussing the final projects for those that want to (not mandatory). 
           
2023-06-15 24:00   CAS Project final version submission deadline    Link to upload on Ilias
2023-07-31 24:00   CAS Completion notification   Comes by email. CAS Certificate comes by post later.
 2023-08-25 17:00-22:00 TBD Graduation Event

 

 

 

Modules 1 to 5 are for all participant the same. In addition one has to attend one elective Module 6. Elective modules in the 2022/2023 Schedule are "Mathematical Methods", "Natural Language Processing" and "Machine Learning for Computer Vision". To be awarded credits for an elective module, students are required to complete one elective fulfilling the requirements of Attendance (75%) as well as Project Work. 

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
April (TBD) TBD TBD Introduction to CAS AML PD Dr. S. Haug Link to Zoom Meeting will appear here
May (TBD) TBD TBD Introduction to CAS AML PD Dr. S. Haug Link to Zoom Meeting will appear here
June (TBD) TBD TBD Introduction to CAS AML PD Dr. S. Haug Link to Zoom Meeting will appear here

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 
(3 days)
09:15 - 17:00 UT

F006

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)

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 will appear here

Modules

All Information about Modules 1-6. 

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 

(5 Days)

09:15 - 17:00 UT

F-107

Dr. G. Schaller Conti Link to Ilias course will appear here
M1 Project  TBD

Module 2

M2 Deep Networks 2023-08-29 - 2023-09-01 
(4 Days)
09:15 - 17:00 UniM

120

Dr. G. Schaller Conti Link to Ilias course will appear here
M2 Project TBD

Module 3

M3 Deep Learning Research  2023-10-02 - 2023-10- 06
(5 Days)
08:30 - 12:30
17:00 - 19:00
TBD (Mediterranean coast) Dr. M. Vladymyrov, Dr. L. Brigato, PD Dr. S. Haug et al. Link to Ilias course will appear here. 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 TBD Dr. M. Vladymyrov, PD Dr. S. Haug, Dr. L. Brigato Link to Ilias course will appear here
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 TBD Prof. Dr. Dr. C. Beisbart Link to Ilias course will appear here
M5 Project TBD

Module 6

M6 Elective Module 2024-01-22 - 2024-01-26
(5 Days)
08:30 - 12:30
17:00 - 19:00
Hotel Regina Mürren
(Bernese Oberland)
TBD Link to Ilias course will appear here. On Friday the module ends at 12:30
M6 Project TBD

Final Project

Final Project deadline TBD

Events and other important dates

Informal events, other dates
Title Date Time Location Comments

CAS Apero

2023-08-25 TBD TBD Come together and have a drink or two

CAS Completion Notification

2024-07-31 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

Employees & Students 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

Vorherige