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Introduction to Deep Learning

2021-09-27

HT2021, 4.5 ECTS

Course description

Recent development in machine learning have led to a surge of interest in artificial neural networks (ANN). New efficient algorithms and increasingly powerful hardware has made it possible to create very complex and high-performing ANNs. The process of training such complex networks has become known as deep learning and the complex networks are typically called deep neural networks.

The aim of this course is to introduce students to common deep learnings architectues such as multi-layer perceptrons, convolutional neural networks and recurrent models such as the LSTM. Basic concepts in machine learning till also be introduced. The course consists of a series of lectures and computer exercises. The programming environment will be python (Jupyter notebook) together with the deep learning libraries Keras and Tensorflow.

The course will be given in flipped classroom mode, with students watching recorded lectures together with online quiz meetings with discussions.

Pre-requisites/requirements

  • Programming: Basic knowledge.
  • Mathematics: Calculus in one and several variables and linear algebra
  • Standard desktop/laptop computer and internet connection

Schedule

27 Sept10:15-12:00Introduction to ML and DL
1 Oct14:15-15:00The MLP-1
4 Oct10:15-12:00The MLP-2
7 Oct14:15-15:00CNN, part 1
8 Oct14:15-15:00CNN, part 2
11 Oct10:15-12:00Autoencoder and GAN
14 Oct14:15-15:00Recurrent networks
22 Oct13:15-17:00Presentations of project work
25 Oct 9:15-17:00Presentations of project work

All teaching events will be online.

Personnel

Course organiser: Mattias Ohlsson

Teachers: Mattias Ohlsson and Patrik Edén

Examination

Written report and oral presentation of a deep learning computer project

Registration

Registration closes on the 19th September 2021 (hard deadline).

Registration is now closed.

 

 


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