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Future IoT Technologies (5 cr)

Code: TTC8850-3004

General information


Enrollment

20.11.2023 - 04.01.2024

Timing

08.01.2024 - 30.04.2024

Number of ECTS credits allocated

5 op

Mode of delivery

Face-to-face

Unit

School of Technology

Campus

Lutakko Campus

Teaching languages

  • English

Seats

0 - 35

Degree programmes

  • Bachelor's Degree Programme in Information and Communications Technology
  • Bachelor's Degree Programme in Information and Communications Technology

Teachers

  • Jouko Kotkansalo

Groups

  • TTV21S3
    Tieto- ja viestintätekniikka (AMK)
  • TTV21S5
    Tieto- ja viestintätekniikka (AMK)
  • TIC21S1
    Bachelor's Degree Programme in Information and Communications Technology
  • TTV21S2
    Tieto- ja viestintätekniikka (AMK)
  • TTV21S1
    Tieto- ja viestintätekniikka (AMK)
  • 09.01.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 16.01.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 23.01.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 30.01.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 06.02.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 13.02.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 20.02.2024 14:30 - 17:00, Future IoT Technologies TTC8850-3004
  • 05.03.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 12.03.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 19.03.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 26.03.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 02.04.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 09.04.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 16.04.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 23.04.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004
  • 30.04.2024 14:15 - 16:45, Future IoT Technologies TTC8850-3004

Objectives

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

Evaluation criteria, satisfactory (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

Evaluation criteria, good (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.

Evaluation 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.

Prerequisites

Johdanto IoT-järjestelmiin