Deep LearningLaajuus (5 cr)
Code: TTC8060
Credits
5 op
Teaching language
- Finnish
Responsible person
- Juha Peltomäki
Objective
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
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Enrollment
01.08.2023 - 24.08.2023
Timing
09.10.2023 - 03.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
School of Technology
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
TTV21S3Tieto- ja viestintätekniikka (AMK)
-
TTV21S5Tieto- ja viestintätekniikka (AMK)
-
TTV21SMTieto- ja viestintätekniikka (AMK)
-
TTV21S2Tieto- ja viestintätekniikka (AMK)
-
TTV21S1Tieto- ja viestintätekniikka (AMK)
-
ZJA23STIDA2Avoin amk, Data-analytiikka 2, Verkko
Objective
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
Location and time
The course will be implemented in the fall semester of 2023.
Materials
The material for the assignments and the content to be studied will be shared during the course. The course utilizes the Python 3.9+ environment, Git version control, scikit-learn, TensorFlow 2, Keras and other visualization and deep learning libraries.
Teaching methods
Virtual study including doing assignments and familiarizing yourself with related lecture and example materials.
Employer connections
The aim is to connect the content of the course to problems that occur in working life.
Completion alternatives
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
Student workload
The workload of one credit corresponds to 27 hours of study. The total amount of study work (5 ECTS) in the course is 135 hours.
Content scheduling
The learning materials are published at the start of the course and are supplemented if necessary during the course.
More detailed installation instructions for the environments used in the course are also given at the beginning of the course.
Further information
The course is evaluated using returned assignments.
The assessment methods are reviewed at the beginning of the course.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Timing
13.02.2023 - 16.04.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
School of Technology
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA23KTIDA2Avoin amk, Data-analytiikka 2, Verkko
Objective
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
Location and time
Opintojakso toteutetaan kevätlukukaudella 2023.
Materials
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.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam schedules
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.
Completion alternatives
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
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Enrollment
01.11.2022 - 05.01.2023
Timing
13.02.2023 - 16.04.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 30
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Objective
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
Location and time
Opintojakso toteutetaan kevätlukukaudella 2023.
Materials
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.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam schedules
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.
Completion alternatives
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
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Timing
07.11.2022 - 20.12.2022
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
School of Technology
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA22STIDA2Avoin amk, Data-analytiikka 2, Verkko
Objective
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
Location and time
Opintojakso toteutetaan syyslukukaudella 2022.
Materials
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-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.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam schedules
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.
Completion alternatives
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
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Enrollment
01.08.2022 - 25.08.2022
Timing
07.11.2022 - 20.12.2022
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Distance learning
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Objective
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
Location and time
Opintojakso toteutetaan syyslukukaudella 2022.
Materials
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.8+-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.
Employer connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam schedules
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla.
Completion alternatives
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
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.
Enrollment
01.11.2021 - 09.01.2022
Timing
07.03.2022 - 30.04.2022
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA21STIDAAvoin AMK, tekniikka, ICT, Data-analytiikka
-
TTV19SMTieto- ja viestintätekniikka
-
TTV19S1Tieto- ja viestintätekniikka
-
TTV20SMTieto- ja viestintätekniikka
-
TTV19S3Tieto- ja viestintätekniikka
-
TTV19S2Tieto- ja viestintätekniikka
-
TTV19S5Tieto- ja viestintätekniikka
Objective
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
Location and time
Opintojakso toteutetaan kevätlukukaudella 2022.
Materials
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Kurssilla hyödynnetään Python 3.7+-ympäristöä, git-versiohallintaa, scikit-learn, tensorflow ja muita koneoppimis- sekä syväoppimiskirjastoja.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyvään sisältöön perehtymisen.
Employer connections
Kurssin sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam schedules
Kurssi arvioidaan palautettujen harjoitustehtävien avulla.
Completion alternatives
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.
Further information
Arvosana määräytyy alla olevien osaamistasojen mukaisesti:
Erinomainen 5: Opiskelija tunnistaa syväoppimisen tuomat hyödyt digitalisaation aikakautena. Opiskelija osaa syväoppimisen yleisimmin käytetyt tekniikat ja osaa kriittisesti perustella käytettyjen tekniikoiden käytön erilaisissa käyttötapauksissa. Hän osaa kriittisesti perustella ja valita oikeat tekniikat syväoppimiseen riippumatta lähdeaineistosta ja osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida kriittisesti toteutuksensa ja perustella sen kehittämistä.
Kiitettevä 4: Opiskelija tunnistaa syväoppimisen tuomat hyödyt digitalisaation aikakautena. Opiskelija osaa syväoppimisen yleisimmin käytetyt tekniikat ja osaa laajasti perustella käytettyjen tekniikoiden käytön erilaisissa käyttötapauksissa. Hän osaa monipuolisesti perustella ja valita oikeat tekniikat syväoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida perusteellisesti toteutuksensa ja perustella sen kehittämistä.
Hyvä 3: Opiskelija tiedostaa syväoppimisen hyödyt digitalisaation aikakautena. Opiskelija tietää syväoppimisen yleisimmin käytetyt tekniikat erilaisissa käyttötapauksissa. Hän osaa perustella ja valita tekniikat syväoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida toteutuksensa ja perustella sen kehittämistä.
Tyydyttävä 2: Opiskelija tietää syväoppimisen yleisimmin käytetyt tekniikat käyttötapauksissa. Hän osaa valita tekniikat syväoppimiseen ja hän osaa soveltaa teknistä osaamistaan käytännössä. Lisäksi opiskelija osaa arvioida pintapuolisesti toteutuksensa.
Välttävä 1: Opiskelija tietää syväoppimisen yleisimmin käytetyt tekniikat käyttötapauksissa. Hän osaa soveltaa yleisimpiä tekniikoita syväoppimisessa. Lisäksi opiskelija osaa arvioida suppeasti toteutuksensa.
Hylätty 0: Opiskelija ei hallitse aihealuetta.
Evaluation scale
0-5
Assessment criteria, satisfactory (1)
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.
Assessment criteria, good (3)
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.
Assessment 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.
Qualifications
Basics in ICT, programming, knowledge and know-how of Python programming language.
Additionally, courses in Data Preprocessing and Computational algorithms.