Uudet teknologiat (5 op)

Arviointiasteikko

H-5

Sisällön jaksotus

The applied artificial intelligence course offers students a specialization in artificial intelligence (AI) for engineering application domains. This course requires a strong background in understanding the theoretical foundations of AI, together with an understanding of mathematics and computer engineering applications for intelligent networks, autonomous robotics, computer vision, and biomedical engineering. Theoretical and technical knowledge is combined with hands-on experience in implementing practical applications.

Tavoitteet

Opintojakson suoritettuaan opiskelija:
-ymmärtää uusimpia IoT-järjestelmiä
-osaa itsenäisesti hankkia tietoa kehittyneistä IoT-arkkitehtuureista ja ratkaisuista
-ymmärtää eri langattomien IoT-järjestelmien erot ja mahdollisuudet

Sisältö

-Uusimmat IoT-ratkaisut
-Langattomat vaihtoehdot IoT-järjestelmissä

Oppimateriaalit

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, Book by Aurelien Geron
Deep Learning with Python, Book by François Chollet
Neural Networks and Deep Learning, Book by Michael Nielsen
Deep Learning, Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Opetusmenetelmät

Lectures (contact teaching)
Hands-on materials
Project work
Self Study Materials

Tenttien ajankohdat ja uusintamahdollisuudet

The final evaluation is based on individual students' project work assigned during the lifecycle of the course.

Opiskelijan ajankäyttö ja kuormitus

2 Lecture Days, approximately 4h/day, have been planned for this course. Self-study materials will be given as well.

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, and basics of reinforcement learning

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

Hylätty (0)

The final evaluation will be based on the student's performance and final project work including a written report. The evaluation metric for this course will be based on Pass / Fail.

Lisätiedot

This is an advanced course and requires students sufficient background in Machine Learning, or at least basics of data analysis and probability concept. Python programming skills are essential for this course.