Introduction to Machine LearningLaajuus (3 op)
Tunnus: TX00DB91
Laajuus
3 op
Osaamistavoitteet
Knowledge and understanding
The students will know some basic concept and some algorithms of machine learning for regression and classification and they will understand how to implement them in Python. They also will know an artificial neural network model for handwritten character recognition and they will be able to implement both the neural network learning and recognition algorithm in Python. To gain a deeper understanding of the basics of neural networks, we don't use the modern frameworks commonly used to implement deep learning.
Skills
The students are able to understand basic machine learning algorithms especially artificial
neural networks and make use of Python for implementation of the algorithms.
Sisältö
Core content level
Introduction to Python programming for implementation of machine learning algorithm.
Introduction to artificial neural networks.
Formal neuron and perceptron.
Simple classification using a single-layer perceptron network.
Multi-layer perceptron network and feed-forward network functions.
Neural network learning based on back-propagation algorithms.
Handwritten character recognition using neural network algorithms.
Supplementary explanation towards deep neural networks.
Additional content
Recognition of handwritten characters written by the participants using the neural network models.
Esitietovaatimukset
Basic mathematics, for example, linear algebra, vector and matrix operations, linear
combination, basic multivariate calculus, and so on. It is preferable to have a basic
programming skill in Python and to be able to use basic data structures and algorithms.
Arviointikriteerit, tyydyttävä (1)
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Arviointikriteeri, hyväksytty/hylätty
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Ilmoittautumisaika
06.05.2024 - 07.08.2024
Ajoitus
12.08.2024 - 16.08.2024
Opintopistemäärä
3 op
Toteutustapa
Lähiopetus
Yksikkö
ICT ja tuotantotalous
Toimipiste
Leiritie 1
Opetuskielet
- Englanti
Paikat
0 - 40
Opettaja
- Akihiro Yamashita
Ryhmät
-
ICTSUMMERICT Summer School
Tavoitteet
Knowledge and understanding
The students will know some basic concept and some algorithms of machine learning for regression and classification and they will understand how to implement them in Python. They also will know an artificial neural network model for handwritten character recognition and they will be able to implement both the neural network learning and recognition algorithm in Python. To gain a deeper understanding of the basics of neural networks, we don't use the modern frameworks commonly used to implement deep learning.
Skills
The students are able to understand basic machine learning algorithms especially artificial
neural networks and make use of Python for implementation of the algorithms.
Sisältö
Core content level
Introduction to Python programming for implementation of machine learning algorithm.
Introduction to artificial neural networks.
Formal neuron and perceptron.
Simple classification using a single-layer perceptron network.
Multi-layer perceptron network and feed-forward network functions.
Neural network learning based on back-propagation algorithms.
Handwritten character recognition using neural network algorithms.
Supplementary explanation towards deep neural networks.
Additional content
Recognition of handwritten characters written by the participants using the neural network models.
Arviointiasteikko
0-5
Arviointikriteerit, tyydyttävä (1)
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Arviointikriteeri, hyväksytty/hylätty
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Esitietovaatimukset
Basic mathematics, for example, linear algebra, vector and matrix operations, linear
combination, basic multivariate calculus, and so on. It is preferable to have a basic
programming skill in Python and to be able to use basic data structures and algorithms.
Ilmoittautumisaika
02.05.2023 - 03.08.2023
Ajoitus
07.08.2023 - 11.08.2023
Opintopistemäärä
3 op
Toteutustapa
Lähiopetus
Yksikkö
ICT ja tuotantotalous
Toimipiste
Leiritie 1
Opetuskielet
- Englanti
Paikat
0 - 40
Koulutus
- Degree Programme in Information Technology
Opettaja
- Akihiro Yamashita
Ryhmät
-
ICTSUMMERICT Summer School
Tavoitteet
Knowledge and understanding
The students will know some basic concept and some algorithms of machine learning for regression and classification and they will understand how to implement them in Python. They also will know an artificial neural network model for handwritten character recognition and they will be able to implement both the neural network learning and recognition algorithm in Python. To gain a deeper understanding of the basics of neural networks, we don't use the modern frameworks commonly used to implement deep learning.
Skills
The students are able to understand basic machine learning algorithms especially artificial
neural networks and make use of Python for implementation of the algorithms.
Sisältö
Core content level
Introduction to Python programming for implementation of machine learning algorithm.
Introduction to artificial neural networks.
Formal neuron and perceptron.
Simple classification using a single-layer perceptron network.
Multi-layer perceptron network and feed-forward network functions.
Neural network learning based on back-propagation algorithms.
Handwritten character recognition using neural network algorithms.
Supplementary explanation towards deep neural networks.
Additional content
Recognition of handwritten characters written by the participants using the neural network models.
Kansainvälisyys
Course lecturers are Assoc. Prof. Dr. Akihiro Yamashita and Prof. Dr. Hiroyuki Aoki from National Institute of Technology.
Arviointiasteikko
0-5
Arviointikriteerit, tyydyttävä (1)
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Arviointikriteeri, hyväksytty/hylätty
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Esitietovaatimukset
Basic mathematics, for example, linear algebra, vector and matrix operations, linear
combination, basic multivariate calculus, and so on. It is preferable to have a basic
programming skill in Python and to be able to use basic data structures and algorithms.
Ilmoittautumisaika
02.05.2022 - 06.08.2022
Ajoitus
08.08.2022 - 12.08.2022
Opintopistemäärä
3 op
Virtuaaliosuus
3 op
Toteutustapa
Etäopetus
Yksikkö
ICT ja tuotantotalous
Toimipiste
Leiritie 1
Opetuskielet
- Englanti
Paikat
0 - 40
Koulutus
- Degree Programme in Information Technology
- Tieto- ja viestintätekniikan tutkinto-ohjelma
Opettaja
- Akihiro Yamashita
Ryhmät
-
ICTSUMMERICT Summer School
Tavoitteet
Knowledge and understanding
The students will know some basic concept and some algorithms of machine learning for regression and classification and they will understand how to implement them in Python. They also will know an artificial neural network model for handwritten character recognition and they will be able to implement both the neural network learning and recognition algorithm in Python. To gain a deeper understanding of the basics of neural networks, we don't use the modern frameworks commonly used to implement deep learning.
Skills
The students are able to understand basic machine learning algorithms especially artificial
neural networks and make use of Python for implementation of the algorithms.
Sisältö
Core content level
Introduction to Python programming for implementation of machine learning algorithm.
Introduction to artificial neural networks.
Formal neuron and perceptron.
Simple classification using a single-layer perceptron network.
Multi-layer perceptron network and feed-forward network functions.
Neural network learning based on back-propagation algorithms.
Handwritten character recognition using neural network algorithms.
Supplementary explanation towards deep neural networks.
Additional content
Recognition of handwritten characters written by the participants using the neural network models.
Kansainvälisyys
Course lecturers are Assoc. Prof. Dr. Akihiro Yamashita and Prof. Dr. Hiroyuki Aoki from National Institute of Technology.
Arviointiasteikko
0-5
Arviointikriteerit, tyydyttävä (1)
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Arviointikriteeri, hyväksytty/hylätty
Daily exercises assigned on the course are worth 50% and both a report and products (i.e.
program source code, sample dataset, experimental results, and so on) about handwritten
character recognition are worth 50%.
Esitietovaatimukset
Basic mathematics, for example, linear algebra, vector and matrix operations, linear
combination, basic multivariate calculus, and so on. It is preferable to have a basic
programming skill in Python and to be able to use basic data structures and algorithms.