Curricula

Objectives

You understand the significance of deep learning in the digitalized operational environment. You know about the most common methods of deep learning and how to apply them in practice to existing data as well as interpret the results of the methods.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Research and information retrieval

Content

- Various neural networks and their architectures and uses (e.g. CNN, RNN, LSTM, Autoencoder).
- Work with open source neural network tools
- Transfer learning
- Prediction
- Machine vision
- NLP

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.9+-ympäristöä, Git-versiohallintaa, scikit-learn, TensorFlow 2, Keras sekä muita visualisointi- ja syväoppimiskirjastoja.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (5 op) kurssilla on 135 tuntia.

Content scheduling

Oppimateriaalit jaetaan kurssin alkaessa ja niitä täydennetään tarvittaessa kurssin aikana.
Myös tarkemmat opintojaksolla käytettävien ympäristöjen asennusohjeet jaetaan alkuvaiheessa.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Satisfactory 2: You know the most commonly used techniques in Deep Learning for various problems. You know how to choose the techniques of Deep Learning and apply their technical know-how in practice. In addition, you know how to assess their implementation superficially.

Sufficient 1: You know the most commonly used techniques of Deep Learning. You are able to apply the most common techniques of Deep Learning. Additionally, you are able to assess their implementation briefly.

Evaluation criteria, good (3-4)

Very good 4: You recognize the advantages of Deep Learning in the era of digitalization. You know the most commonly used techniques of Deep Learning and is able to justify versatile the use of the implemented techniques in various tasks. You know how to apply their technical know-how in practice and assess their implementation critically as well as validate its development.

Good 3: You are aware of the advantages of Deep Learning in the era of digitalization. You know the most commonly used techniques of Deep Learning for various problems. You are able to apply his/her technical know-how in practice, assess their implementation in practice and validate its development.

Evaluation criteria, excellent (5)

Excellent 5: You recognize the advantages of Deep Learning in the era of digitalization. You know the most commonly used techniques and is able to critically justify the used techniques in various tasks. You are able to apply their technical know-how in practice and critically assess their implementation as well as validate its development.

Prerequisites

Basics in ICT, programming, knowledge and know-how of Python programming language.

Additionally, courses in Data Preprocessing and Computational algorithms.