Future IoT Technologies (5 cr)
Code: TTC8850-3003
General information
Enrollment
01.08.2023 - 24.08.2023
Timing
28.08.2023 - 19.12.2023
Number of ECTS credits allocated
5 op
Virtual portion
5 op
Mode of delivery
Online learning
Unit
School of Technology
Teaching languages
- English
Seats
0 - 35
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Jouko Kotkansalo
Groups
-
TTV21S3Tieto- ja viestintätekniikka (AMK)
-
TTV21S5Tieto- ja viestintätekniikka (AMK)
-
TTV21SMTieto- ja viestintätekniikka (AMK)
-
TTV21S2Tieto- ja viestintätekniikka (AMK)
-
TTV21S1Tieto- ja viestintätekniikka (AMK)
- 21.11.2023 12:15 - 15:00, Future IoT Technologies TTC8850-3003
- 28.11.2023 12:15 - 15:00, Future IoT Technologies TTC8850-3003
- 05.12.2023 12:15 - 15:00, Future IoT Technologies TTC8850-3003
- 12.12.2023 12:15 - 15:00, Future IoT Technologies TTC8850-3003
- 19.12.2023 12:15 - 15:00, Future IoT Technologies TTC8850-3003
Objective
Course objectives
In the course, you will learn how to use advanced new technologies, such as artificial intelligence, in the implementation of IoT systems. You get to know different neural networks and understand their basic parameters.
Competences:
EUR-ACE Knowledge and Understanding
EUR-ACE Engineering Practice
You know the architectures of neural networks and understand their application areas. You know how to make a Q-table of basic neural network design and implementation in practice. You know what the RL agent does and you know how to train the agent to make it work optimally. You understand the difference between On-Policy and Off-Policy algorithms and know how to use information to select an algorithm.
Content
Comparison of neural networks (RL, Supervised, Unsupervised )
Keras, Pytorch and other algorithm-based neural networks
RL agent and environment
Exploitation and exploration
Reward and hyperparameters
Basic neuromath and activation functions
Policy gradient method and value based learning
Bellman equation and its demo (Q-learning)
Pytorch, PPO algorithm and demo
Evaluation scale
0-5
Arviointikriteerit, tyydyttävä (1-2)
Avoidance 1: You understand the principles of neural networks and can apply the knowledge to select them
Satisfactory 2: You understand the basics of Q-learning methods and can make a small application with it
Arviointikriteerit, hyvä (3-4)
Good 3: Can adjust the parameter of the Bellman algorithm (Exploration, Exploitation, gamma +....) so that the operation of the controlled object is optimized
Excellent 4: You know how to tune a multilayer neural network using the PPO algorithm so that the given actuator behaves as desired.
Assessment criteria, excellent (5)
Excellent 5: In addition to the previous ones, you understand the limitations of artificial intelligence in various practical applications and understand that OpenAI-Gym models require a strong substance control. You also know how to document your work well, using TensorFlow outputs to evaluate hyperparameters.
Qualifications
Johdanto IoT-järjestelmiin