MACHINE LEARNING
| Title | ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ / MACHINE LEARNING |
| Code | NIS-08-04 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Grigorios Tsoumakas |
| Common | No |
| Status | Active |
| Course ID | 600014928 |
Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)
Registered students: 22
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| GENIKĪ KATEUTHYNSĪ | YPOCΗREŌTIKO KATA EPILOGĪ | 8 | 4 | 5 |
| Academic Year | 2018 – 2019 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600138536
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600138536
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=11553
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
Required Courses
- NCO-01-05 BASIC PROGRAMMING PRINCIPLES
- NCO-02-02 PROBABILITIES & STATISTICS
Learning Outcomes
Upon successful completion of the course, students will know what is involved in the field of engineering learning, as well as how algorithms for linear models, tree models, rule models, ensembles of models and reinforcement learning work. In addition, they will be able to apply such algorithms to real-world data and applications using Python's scikit-learn and gym libraries.
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
- Work in teams
- Work in an interdisciplinary team
- Appreciate diversity and multiculturality
- Demonstrate social, professional and ethical commitment and sensitivity to gender issues
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to Machine Learning. Linear Models. Tree Models. Rule Models. Model Ensembles. Reinforcement Learning.
Keywords
machine learning, linear models, decision trees, rule learning, ensemble methods, reinforcement learning
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
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
- Use of ICT in Student Assessment
Description
Slides, notebooks with code and comments, use of elearning platform of AUTH, practical exercises and assignments
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 39 | ✓ | |||
| Project | 69 | ✓ | |||
| Exams | 3 | ✓ | |||
| Total | 150 |
Student Assessment
Description
The evaluation process will be based on the final written examination and on two projects that will involve the implementation of machine learning algorithms.
Student Assessment methods
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
- Δεν βρέθηκε κατάλληλο σύγγραμα στον Εύδοξο
Additional bibliography for study
- Σημειώσεις μαθήματος
- Machine Learning, Tom Mitchell, McGraw-Hill Education, 1997.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, O'Reilly Media, 2017.
Last Update
27-02-2020
