Fundamentals of Deep Learning (5 op)
Toteutuksen tunnus: TT00EO93-3003
Toteutuksen perustiedot
- Ajoitus
- 01.01.2022 - 31.12.2022
- Toteutus on päättynyt.
- Opintopistemäärä
- 5 op
- Virtuaaliosuus
- 5 op
- Toteutustapa
- Etäopetus
- Toimipiste
- Karaportti 2
- Opetuskielet
- englanti
- Paikat
- 0 - 5000
- Koulutus
- Tieto- ja viestintätekniikan tutkinto-ohjelma
- Opettajat
- Virve Prami
- Opintojakso
- TT00EO93
Aika ja paikka
Course can be done in own pace in TechClass environment.
Oppimateriaalit
Online.
Harjoittelu- ja työelämäyhteistyö
N/A
Tenttien ajankohdat ja uusintamahdollisuudet
Online.
Kansainvälisyys
N/A
Toteutuksen valinnaiset suoritustavat
N/A
Arviointimenetelmät ja arvioinnin perusteet
Exercises 50%
Quizzes 25%
Project 25%
Opiskelijan ajankäyttö ja kuormitus
Lectures = 40h
Exercises = 15h
Self-study = 40h
Quizzes = 10h
Project = 30h
Total = 135 hours
Sisällön jaksotus
Up to student her-/himself.
Opetusmenetelmät
100% online Self-Study course.
- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study
Arviointiasteikko
Hyväksytty/Hylätty
Arviointikriteerit, tyydyttävä (1)
- The student knows the basic definitions of Deep Learning.
- The student knows the general difference between Machine Learning and Deep Learning.
- The student knows the primary types of Deep Learning methods.
- The student is familiar with the concept of mapping that learning algorithms perform.
- The student is familiar with variations of Neural Network algorithms used for different applications.
Arviointikriteerit, hyvä (3)
- The student is familiar with basic concepts of the Neural Network algorithm such as wight and neuron.
- The student knows the reasons for the advent of Deep Learning and its history.
- The student knows the intuitions behind Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- The student is familiar with the steps of training and evaluating Neural Networks.
- The student knows the applications of CNNs and RNNs.
Arviointikriteerit, kiitettävä (5)
- The student understands the concepts and intuitions behind the Neural Network algorithm.
- The student knows the concepts of regularization, overfitting, and hyperparameters selection.
- The student knows layers of CNNs.
- The student is familiar with the popular architectures of CNNs.