Introduction to Machine Learning (3 ECTS)
Code: TX00DB91-3009
General information
- Enrollment
- 05.05.2025 - 06.08.2025
-
Enrollment is ongoing
Enroll to the implementation in OMA
- Timing
- 11.08.2025 - 15.08.2025
- The implementation has not yet started.
- Number of ECTS credits allocated
- 3 ECTS
- Mode of delivery
- On-campus
- Unit
- School of ICT and Industrial Management
- Campus
- Leiritie 1
- Teaching languages
- English
- Seats
- 0 - 40
- Teachers
- Akihiro Yamashita
- Course
- TX00DB91
Implementation has 5 reservations. Total duration of reservations is 20 h 0 min.
| Time | Topic | Location |
|---|---|---|
|
Mon 11.08.2025 time 09:00 - 13:00 (4 h 0 min) |
Introduction to Machine Learning TX00DB91-3009 |
MMC314
IT-Tila
|
|
Tue 12.08.2025 time 09:00 - 13:00 (4 h 0 min) |
Introduction to Machine Learning TX00DB91-3009 |
MMC314
IT-Tila
|
|
Wed 13.08.2025 time 09:00 - 13:00 (4 h 0 min) |
Introduction to Machine Learning TX00DB91-3009 |
MMC314
IT-Tila
|
|
Thu 14.08.2025 time 09:00 - 13:00 (4 h 0 min) |
Introduction to Machine Learning TX00DB91-3009 |
MMC314
IT-Tila
|
|
Fri 15.08.2025 time 09:00 - 13:00 (4 h 0 min) |
Introduction to Machine Learning TX00DB91-3009 |
MMC314
IT-Tila
|
Learning outcomes
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.
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%.