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Deep Learning

July 5-9, 2021 Online

 


Faculty

Eran Raviv holds a PhD in econometrics from Erasmus University Rotterdam, a master’s degree in applied statistics from Tel Aviv University and a second master’s degree in quantitative finance from Erasmus University Rotterdam. His research has been published in high impact peer-reviewed journals.
Meet the Lecturer

Course

This one-week Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures and application examples.
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in how the methods learnt in class can actually be applied to data. The lab sessions are meant to work on the assignments, such that the students automatically keep up with the material.

Level

The summer course welcomes Master’s and PhD students, alumni, professionals in economics and related fields, who are interested in deep learning. The level is introductory, targeted at participants who would like to familiarize themselves with the topic, and acquire a good basis from which to approach deep learning potential applications.

Topics covered

  • Introduction to Deep Learning (High-level definitions of fundamental concepts and first examples)
  • Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
  • Feed forward neural networks
  • Convolutional neural networks
  • Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
  • Recurrent neural networks
  • Long-short term memory units
  • Advanced architectures (Densely connected networks, Adaptive structural learning)

Admission requirements

Students are expected to have a background in calculus and in linear algebra. Familiarity with open source languages such as R or Python is a must.

 

Academic director Eran Raviv
Degree programme Certificate
Credits Participants who joined at least 80% of all sessions and hand in the assignment will receive a certificate of participation stating that the summer school is equivalent to a work load of 3 ECTS. Note that it is the student’s own responsibility to get these credits registered at their university.
Mode Short-term
Language English
Venue Zoom
Capacity 30 participants (minimum of 12)
Fees Tuition Fees and Payment
Application deadline June 14
Apply here closed

Contact

Summer School