New Technologies (5 cr)
Code: MS00BP22-3002
General information
Enrollment
05.12.2020 - 18.01.2021
Timing
04.01.2021 - 01.05.2021
Number of ECTS credits allocated
5 op
RDI portion
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Teaching languages
- Finnish
Seats
0 - 60
Degree programmes
- Master of Business Administration, Software Engineering and ICT
- Master of Engineering, Software Engineering and ICT
Teachers
- Mojtaba Jafaritadi
Groups
-
YICTTK21
-
YICTIK21
Objective
After completing the course, the student is able to:
-understand new IoT systems
-learn by themselves advanced IoT architectures and solutions
-understand differences and possibilities in new wireless IoT systems
Content
-Recent solutions for IoT
-Wireless IoT solutions, differences and possibilities
Materials
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
Teaching methods
Lectures (contact teaching)
Hands-on materials
Project work
Self Study Materials
Exam schedules
The final evaluation is based on individual students' project work assigned during the lifecycle of the course.
Student workload
2 Lecture Days, approximately 4h/day, have been planned for this course. Self-study materials will be given as well.
Content scheduling
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.
Further information
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.
Evaluation scale
H-5
Assessment methods and criteria
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
Assessment criteria, fail (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.