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FYTN14/EXTQ40/NTF005F: Introduction to Artificial Neural Networks and Deep Learning, 7.5 credits

given by the Department of Astronomy and Theoretical Physics.

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Contents

The course covers the most common models in artificial neural networks with a focus on the multi-layer perceptron. The course also provides an introduction to deep learning. Selected topics:

Feed-forward neural networks
The simple perceptron and the multi-layer perceptron, choice of suitable error functions and techniques to minimize them, how to detect and avoid overtraining, ensembles of neural networks and techniques to create them, Bayesian training of multi-layer perceptrons

Recurrent neural networks
Simple recurrent networks and their use in time series analysis, fully recurrent for both time series analysis and associative memories (Hopfield model), the simulated annealing optimization technique

Self-organizing neural networks
Networks that can extract principal components, networks for data clustering, learning vector quantization (LVQ), self-organizing feature maps (SOFM)

Deep learning
Overview of deep learning, convolutional neural networks for classification of images, different techniques to avoid overtraining in deep networks, techniques to pre-train deep networks

General information

Semester: autumn
Study period: 2
Level: master
Language: English
Form of teaching: Lectures, computer exercises, problem solving sessions
Assessment: Written exam, computer exercise reports
Grading scale: U-G-VG
Course plan: FYTN14 in English, FYTN14 in Swedish, EXTQ40 in English, EXTQ40 in Swedish
Course plan for PhD course: NTF005F in English, NTF005F in Swedish

Course literature

Printed lecture notes.
Supplemental online litterature "Deep Learning Book"

Teachers

Mattias Ohlsson, mattias@thep.lu.se
Patrik Edén, patrik@thep.lu.se

Introductory Meetings

Information about the compulsory introductory meetings can be found hereLinks to an external site..

Schedule

Through Time Edit, though changes during the course may not be shown.

Application

Through antagning.se