ARTIFICIAL LEARNING
| Title | ΤΕΧΝΙΚΕΣ ΜΗΧΑΝΙΚΗΣ ΜΑΘΗΣΗΣ / ARTIFICIAL LEARNING |
| Code | ΔΜ1025 |
| Interdepartmental Programme | PPS Advanced Computer and Communication Systems |
| Collaborating Schools | Electrical and Computer Engineering Medicine Music Studies Journalism and Mass Communications |
| Cycle / Level | 1st / Undergraduate, 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Christos Diou |
| Common | Yes |
| Status | Active |
| Course ID | 600004438 |
Programme of Study: PPS Advanced Computer and Communication Systems
Registered students: 13
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| EUFYĪ KAI AUTONOMA SYSTĪMATA-METHODOLOGIES YPOLOGISTIKĪS NOĪMOSYNĪS KAI EFARMOGES | Compulsory Course | 1 | 1 | 5 |
Programme of Study: PPS Advanced Computer and Communication Systems
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Eyfyī systīmata-Methodologíes ypologistikīs noīmosýnīs kai efarmogés | Compulsory Course | 2 | 1 | 5 |
| Diktyakī Ypologistikī- Īlektronikó Ebório | Elective Courses | 2 | 1 | 5 |
| Academic Year | 2020 – 2021 |
| Class Period | Winter |
| Faculty Instructors | |
| Instructors from Other Categories | |
| Weekly Hours | 24 |
| Class ID | 600177742
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600177742
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Prerequisites
General Prerequisites
Calculus of single and many variable functions, linear algebra. Basic programming skills.
Learning Outcomes
This course aims at introducing machine learning algorithms and techniques, including theory and practicall applications. The course includes:
- Single and multiple linear regression, as well as their nonlinear variants
- Logistic regression and Linear Discriminant Analysis for classification
- Capacity control and regularization
- Nonparametric classifiers and introduction to Support Vector Machines
- Nonlinear SVMs and Kernels
- Other SVM variants
- Ensemble learning methods
- Random forests, EXTRA trees, gradient boosting trees
- Practical applications, hyperparameter selection and evaluation metrics
The course includes practical examples in Python, using scikit-learn. Evalution will take place using projects that will be handed out during the course.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Generate new research ideas
- Advance free, creative and causative thinking
Course Content (Syllabus)
The course includes:
- Single and multiple linear regression, as well as their nonlinear variants
- Logistic regression and Linear Discriminant Analysis for classification
- Capacity control and regularization
- Nonparametric classifiers and introduction to Support Vector Machines
- Nonlinear SVMs and Kernels
- Other SVM variants
- Ensemble learning methods
- Random forests, EXTRA trees, gradient boosting trees
- Practical applications, hyperparameter selection and evaluation metrics
The course includes practical examples in Python, using scikit-learn. Evalution will take place using projects that will be handed out during the course.
Educational Material Types
- Slide presentations
- Book
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 26 | ||||
| Total | 26 |
Student Assessment
Description
Individual projects
Bibliography
Additional bibliography for study
1. Christopher Bishop, "Pattern recognition and machine learning". Springer, 2006
2. Alex Smola and S.V.N. Vishwanathan, "Introduction to machine learning", Cambridge university press, 2008
Διαθέσιμο online: http://alex.smola.org/drafts/thebook.pdf
3. Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The elements of statistical learning: Data mining, inference and prediction", 2nd edition, Springer, 2008
Διαθέσιμο online: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
4. Ian Goodfellow, Yoshua Bengio and Aaron Courville, "Deep learning", MIT press, 2016
Διαθέσιμο online: https://www.deeplearningbook.org/
5. Aurelien Geron, "Hands-on machine learning with scikit-learn and Tensorflow: Concepts, tools and techniques to build intelligent systems", O'Reilly, 2017
Last Update
05-02-2019
