Course syllabus

  NBIS workshop in Neural Nets and Deep Learning

 

precourse icon Pre-course information for accepted students

info icon About the course

 

content icon Course content

Topics covered will include:

  • NN building blocks, including concepts such as neurons, activation functions, loss functions, gradient descent and back-propagation
  • Convolutional Neural Networks
  • Recursive Neural Networks
  • Graph Neural Networks
  • Transformers and attention-based models
  • Autoencoders
  • Best practices of NN design and use

Here you fill find a formal outline of the learning outcomes  

 

University credits

NBIS workshops do not officially assign ECTS credits upon completion. Instead, we keep track of attendance and release a certificate to those students who have attended the full workshop. The student can then show the certificate to their PhD supervisor, who will determine the appropriate amount of credits to assign whenever appropriate (a 40 hours, full-time course should correspond to roughly 1.5 credits).

 

schedule icon Important dates

Application opens: 2024-01-25

Application closes: 2024-04-10

Confirmation to accepted students:  2024-04-25

Course dates: 20-24 May 2024

 

map icon  Location

The course is held in person only at BMC Uppsala, room Trippelrummet (see map link below)

Trippelrummet, Navet, BMC Uppsala

 

Fee

The fee for this on-site workshop is 3000 SEK to be paid by invoice to NBIS. Please note that NBIS cannot invoice individuals so we need your institutional invoicing address.

The fee covers lunches, coffee and a course dinner. Those who accept the spot and then do not attend without prior notification will also be invoiced.

Note that travel and accommodation is not included in the fee and must be arranged by the participants.

 

education icon Designed audience

This is a national course open to PhD students, postdocs, group leaders and core facility staff. The course is intended for student that have:

  • Knowledge of programming languages (for example python, R)
  • Previous experience in the fields of Statistics and/or Machine Learning
  • Experience with command-line tools

A more detailed list of prerequisites can be found here.

 

  Course staff and contact info

Please use the course email to contact us: edu.neural-nets-deep-learning@nbis.se

  • Claudio Mirabello (course leader)
  • Christophe Avenel (course leader)
  • Bengt Sennblad
  • Marcin Kierczak
  • Per Unneberg
  • Erik Ylipää

 

image.png Code of conduct

By participating in this workshop, you will be agreeing to the NBIS Code of Conduct.