Tekoälysovellukset (5 op)

Arviointiasteikko

H-5

Sisällön jaksotus

Chapter 1: The Machine Learning Landscape
Chapter 2: End-to-End Machine Learning Project
Chapter 3: Classification
Chapter 4: Training Models
Chapter 5: Support Vector Machines
Chapter 6: Decision Trees
Chapter 7: Ensemble Learning and Random Forests
Chapter 8: Dimensionality Reduction
Chapter 9: Unsupervised Learning Techniques
Chapter 10: Introduction to Artificial Neural Networks with Keras
Chapter 11: Training Deep Neural Networks

Tavoitteet

Opintojakson suoritettuaan opiskelija:
- ymmärtää, mitä tekoäly on sosiaali- ja terveysalalla
- osaa selittää tekoälysovellusten peruskäsitteet
- tuntee käytössä olevia tekoälysovelluksia sosiaali- ja terveysalalla
- ymmärtää tekoälyn mahdollisuudet ja rajoitukset sosiaali- ja terveysalalla

Sisältö

- Tekoälyn perusteet
- Tekoälysovellukset
- Hermoverkot
- Koneoppiminen

Oppimateriaalit

1) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron, OREILLY, 2020

2) Hands-on Scikit-Learn for Machine Learning Applications, by David paper, Apress publications, 2020

Opetusmenetelmät

The course includes theoretical lectures, mandatory practical lab works, and self-study/ home assignments.

"PYTHON IS THE OFFICIAL LANGUAGE OF THE PROGRAMMING ASSIGNMENTS". Try to get some hand in python before and during the course.

NOTE: Due to the current pandemic, remote implementation of the lab works is still a possibility.

Tenttien ajankohdat ja uusintamahdollisuudet

Exam dates will be announced (it will be most likely the last session, the one after the last lecture)

Pedagogiset toimintatavat ja kestävä kehitys

Lectures (onsite and online lectures will be arranged)
Self-study materials from online sources
Project work (OPTIONAL)

Opiskelijan ajankäyttö ja kuormitus

lab/home works 10x2h =20h
lectures 10x2h = 20h
exam =4h
self study and exam preparation 36h
TOTAL 80h

Arviointimenetelmät ja arvioinnin perusteet

By the end of this course, students will:
Obtain practical and technological skills including machine learning, optimization, neural networks, data processing, and advanced AI algorithms

Possess knowledge of state-of-the-art modern topics in artificial intelligence, including Regression, Classification, support vector machines, neural networks, deep networks

Learn a blend of hardware, software, and data analytics skills to support the design and development of AI applications in various engineering domains

In order to pass this course, you must pass the final exam. The final grade will be based on exam and home/lab works:
60% -> 1
68% -> 2
76% -> 3
84% -> 4
92% -> 5
Mandatory lab works: individual Lab performance matters
Mandatory lectures