Deep Learning (TTC8060-3006)

The student understands the significance of deep learning in the digitalized operational environment. The student knows 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.
EA-EN: EUR-ACE Engineering Analysis
EA-MJ: EUR-ACE Making Judgements

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

Additionally, courses in Data Preprocessing and Computational algorithms.

• 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

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

Sufficient 1: The student knows the most commonly used techniques of Deep Learning. The student is able to apply the most common techniques of Deep Learning. Additionally, the student is able to assess their implementation briefly.

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

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

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

Finnish

Opintojakso toteutetaan kevätlukukaudella 2023. (not translated)

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen. (not translated)

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. (not translated)

Juha Peltomäki

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin. (not translated)

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. (not translated)

13.02.2023 - 16.04.2023

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (5 op) kurssilla on 135 tuntia. (not translated)

Oppimateriaalit jaetaan kurssin alkaessa ja niitä täydennetään tarvittaessa kurssin aikana.
Myös tarkemmat opintojaksolla käytettävien ympäristöjen asennusohjeet jaetaan alkuvaiheessa. (not translated)

ZJA23KTIDA2

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä. (not translated)

Bachelor's Degree Programme in Information and Communications Technology

Face-to-face

5 cr

School of Technology