MACHINE LEARNING
| Title | ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ / MACHINE LEARNING |
| Code | IS02 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioannis Vlachavas |
| Common | No |
| Status | Active |
| Course ID | 40002247 |
| Academic Year | 2017 – 2018 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600110508
|
Course Type 2016-2020
- Scientific Area
- Skills Development
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/1/600110508
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=8135
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Students become familiar with the main concepts of machine learning as well as with specific machine learning techniques. They also become familiar with applications of machine learning. They acquire skills in reviewing scientific papers, giving scientific presentations and working in practice with data using relevant machine learning software.
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
Course Content (Syllabus)
Introduction, Concept, Learning, Decision Trees, Rule Learning/Inductive Logic Programming, Instance - Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Fuzzy Learning, Text Mining, Mining Biological Data, Temporal Data Mining, Machine Learning Software.
Keywords
Machine Learning, Supervised learning, Unsupervised Learning, Classification, Regression
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Description
Slides in electronic format, machine learning software
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.3 | ✓ | ||
| Reading Assigment | 42 | 1.4 | ✓ | ||
| Project | 42 | 1.4 | ✓ | ||
| Written assigments | 39 | 1.3 | ✓ | ||
| self-study | 63 | 2.1 | ✓ | ||
| Total | 225 | 7.5 |
Student Assessment
Description
20% review paper and presenation of a machine leanring application area, 20% practical project on analyzing data using software, 60% written exams. These criteria are clearly stated at the course's web page.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Διδακτικές Σημειώσεις
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997
- Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, October 2004.
- Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Addison Wesley. 2005
- Data Mining, Practical Machine Learning Tools and Techniques with Java Implementation (second edition), Ian Witten & Eibe Frank, Morgan Kaufmann, 2005.
- Introduction to Machine Learning (Draft of Incomplete Notes), Nils J. Nilsson, 2015
Last Update
11-05-2018
