Admission requirements
Elementary calculus and linear algebra; basics of probability theory and statistics.
Description
The course provides an introduction to key concepts and algorithms for neural networks, with strong emphasis on Deep Learning and its applications. It covers the following topics:
Part One: Classical Neural Networks
o Basics of statistical pattern recognition
o Linear models: Perceptron, Logistic Regression, Support Vector Machines
o Multi-layer Perceptron and Backpropagation
o RBF-networksPart Two: DeepLearning
o Convolutional Networks
o Autoencoders
o Restricted Boltzmann Machines
o Software and hardware for Deep Learning
Moreover, several state-of-the-art applications of Deep Learning to image recognition, computer vision, language modeling, will be discussed. The course consists of weekly lectures, a few programming assignments (in Python or Matlab) and the final written exam.
Course objectives
The objective of this course are:
to provide a general introduction to the field of neural networks and deep learning and their applications
to develop practical skills for designing and training neural networks for tasks like image classification, speech recognition, forecasting
to learn some popular tools for training deep architectures: Theano, Torch, Pylearn2
Timetable
The most recent timetable can be found at the LIACS website
Mode of instruction
Lectures
Computer Lab
Practical Assignments
Assessment method
The final grade will be the weighted average of grades for:
programming assignments (60%)
written exam (40%
Blackboard
See this Blackboard
Reading list
Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press (in preparation)
Registration
You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.
Contact information
Study coordinator Computer Science, Riet Derogee