Introduction to Machine LearningLaajuus (3 cr)
Code: TX00DB91
Credits
3 op
Objective
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.
Content
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.
Prerequisites
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.
Assessment criteria, satisfactory (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%.
Assessment criteria, approved/failed
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%.
Enrollment
06.05.2024 - 07.08.2024
Timing
12.08.2024 - 16.08.2024
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
School of ICT
Campus
Leiritie 1
Teaching languages
- English
Seats
0 - 40
Degree programmes
- Degree Programme in Information Technology
Teachers
- Akihiro Yamashita
Groups
-
ICTSUMMERICT Summer School
Objective
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.
Content
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.
Evaluation scale
0-5
Assessment criteria, satisfactory (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%.
Assessment criteria, approved/failed
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%.
Prerequisites
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.
Enrollment
02.05.2023 - 03.08.2023
Timing
07.08.2023 - 11.08.2023
Number of ECTS credits allocated
3 op
Mode of delivery
Contact teaching
Unit
School of ICT
Campus
Leiritie 1
Teaching languages
- English
Seats
0 - 40
Degree programmes
- Degree Programme in Information Technology
Teachers
- Akihiro Yamashita
Groups
-
ICTSUMMERICT Summer School
Objective
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.
Content
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.
International connections
Course lecturers are Assoc. Prof. Dr. Akihiro Yamashita and Prof. Dr. Hiroyuki Aoki from National Institute of Technology.
Evaluation scale
0-5
Assessment criteria, satisfactory (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%.
Assessment criteria, approved/failed
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%.
Prerequisites
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.
Enrollment
02.05.2022 - 06.08.2022
Timing
08.08.2022 - 12.08.2022
Number of ECTS credits allocated
3 op
Virtual portion
3 op
Mode of delivery
Distance learning
Unit
School of ICT
Campus
Leiritie 1
Teaching languages
- English
Seats
0 - 40
Degree programmes
- Degree Programme in Information Technology
- Information and Communication Technology
Teachers
- Akihiro Yamashita
Groups
-
ICTSUMMERICT Summer School
Objective
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.
Content
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.
International connections
Course lecturers are Assoc. Prof. Dr. Akihiro Yamashita and Prof. Dr. Hiroyuki Aoki from National Institute of Technology.
Evaluation scale
0-5
Assessment criteria, satisfactory (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%.
Assessment criteria, approved/failed
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%.
Prerequisites
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.