Artificial Intelligence - Robotics
| Title | Τεχνητή Νοημοσύνη - Ρομποτική / Artificial Intelligence - Robotics |
| Code | RM121 |
| Faculty | Agriculture, Forestry and Natural Environment |
| School | Agriculture |
| Cycle / Level | 2nd / Postgraduate, 3rd / Doctorate |
| Teaching Period | Winter/Spring |
| Coordinator | Xanthoula irini Pantazi |
| Common | Yes |
| Status | Active |
| Course ID | 600018834 |
| Academic Year | 2023 – 2024 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Total Hours | 52 |
| Class ID | 600245078
|
Course Type 2021
Specific Foundation
Course Type 2016-2020
- Background
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600245078
- eLearning: https://elearning.auth.gr/course/view.php?id=15986
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
By the end of the course the students are expected to:
1. Fully understand the most well-known artificial intelligence algorithms.
2. Gain familiarized with data pre-processing techniques.
3. Interpret and analyze the results and the performances of artificial intelligence algorithms.
4. Employ the most suitable algorithms and adopt the relative methodologies based on the nature of the problem, automating the solution procedure.
5. Understand the complexity of various solutions applied in the agricultural domain.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
Basic Concepts of Artificial Intelligence, Introduction to Machine Learning, Introduction to Algorithms and Types of Machine Learning, Introduction to Artificial Neural Networks (ANNs), Introduction to MultiLayer Perpepctron network (MLP), Machine Lerning Algorithms Techniques for Training, Introduction to Training rules, Backpropagation training algorithm, RBF networks, Support Vector Machines (SVMs), Introduction to data mining, Introduction to RBFs, Introduction to Self-Organizing networks, Data Fusion, Introduction to Deep Learning techniques, Convolutional Neural Networks (CNNs) Applications of Machine Learning Techniques and ΑΝΝs (computer vision, robotics)
Keywords
data mining, machine learning, artificial neural networks, data analysis, automation
Educational Material Types
- Notes
- Slide presentations
- Video lectures
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Description
Slide presentations
Interactive excersises
2 projects
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 103 | ||||
| Project | 61 | ||||
| Written assigments | 1 | ||||
| Exams | 3 | ||||
| Total | 168 |
Student Assessment
Description
During the semester two assignments on state-of-the-art classification or clustering problems will be provided, to be conducted. The final course grade is related to the performance of students in both two online assignments. The average grade in the two assignments will form the final grade.
Student Assessment methods
- Written Assignment (Formative)
- Labortatory Assignment (Formative)
Last Update
21-01-2024
