MACHINE LEARNING
| Title | Μηχανική Μάθηση / MACHINE LEARNING |
| Code | ΝAKA828Ε |
| Faculty | Agriculture, Forestry and Natural Environment |
| School | Agriculture |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Georgios Menexes |
| Common | No |
| Status | Active |
| Course ID | 600024901 |
Programme of Study: Sustainable Agricultural Systems and Climate Change
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 3 | 2 | 8 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 5 |
| Total Hours | 65 |
| Class ID | 600257125
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600257125
- eLearning: https://eclass.auth.gr/courses/OCRS143/
- OpenCourses (eClass): https://eclass.auth.gr/courses/OCRS143/
- Other: http://users.auth.gr/gmenexes
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Students should be familiar with the use of computers.
Students should have attended courses on Statistics and Agricultural Experimentation.
Learning Outcomes
Upon completion of this course, students will be able to:
1) Explain fundamental ideas of Probability and Statistics and the theory behind the commonly used statistical techniques.
2) Apply suitable statistical techniques correctly for solving biological problems.
3) Analyze data using common statistical software and interpret outputs.
4) Prepare statistical reports and make presentations.
5) Communicate with a statistician.
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 international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Respect natural environment
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to Data Analysis - Basic concepts of Data Analysis - Data collection and storage methods - Data processing and "cleaning". Introduction to Machine Learning. Important problems of Machine Learning. Review of elements of Probability Theory and Linear Algebra. Supervised Learning - Presentation of basic algorithms (Regression, Bayes Classifier, K Nearest Neighbor Classifier, Decision Trees, Support Vector Machines, Neural Networks). Model evaluation, Performance metrics, Overfitting/Underfitting, Normalization, Optimization. Unsupervised Learning - Presentation of basic algorithms (Cluster Analysis, Principal Component Analysis). Introduction to Reinforcement Learning - Introduction to basic algorithms (Markov Decision Processes, Bellman Optimality Criterion, Price Iteration, Policy Iteration). Introduction to Deep Learning - Presentation of basic algorithms (Feed-forward Deep Neural Networks. Convolutional Neural Networks). Case studies – examples – applications in Python and R/Rstudio programming languages. The student can make better use of the knowledge provided in this course if he/she has knowledge from the course "Biometrics II" as well as basic computer skills.
Keywords
Experimental designs, Analysis of Variance, Linear and non linear Regression, Multivariate Data Analysis, Statistical Software
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Multimedia
- Book
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
Web, Internet, Powerpoint, video, Excel, SPSS, educational software-tutorial, zoom,email.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 65 | ✓ | ✓ | ||
| Seminars | 20 | ✓ | ✓ | ||
| Laboratory Work | 20 | ✓ | ✓ | ✓ | |
| Reading Assigment | 30 | ✓ | ✓ | ✓ | |
| Tutorial | 20 | ✓ | ✓ | ✓ | |
| Project | 20 | ✓ | ✓ | ✓ | |
| Written assigments | 20 | ✓ | ✓ | ✓ | |
| Exams | 5 | ✓ | ✓ | ||
| Total | 200 |
Student Assessment
Description
Written exams (60%), Project (20%), Oral exams (10%)
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Oral Exams (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
- Report (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
1) Φωτιάδης, Ν. (1995). "Εισαγωγή στη Στατιστική για βιολογικές επιστήμες". Θεσσαλονίκη: University Studio Preee (Κωδικός Εύδοξος: 17225).
2) Φασούλας, Α. (2008). "Στοιχεία Πειραματικής Στατιστικής". Θεσσαλονίκη: Εκδόσεις Γαρταγάνη.(Κωδικός Εύδοξος: 1944).
Additional bibliography for study
1) Steel, R., Torrie, J. & Dickey, D. (1997). Principles and Procedures of Statistics: A Biometrical Approach. Third Edition. Singapore: McGraw-Hill Book Company.
2) Gomez, K. & Gomez, A. (1984). Statistical Procedures for Agricultural Research. Singapore: John Willey & Sons, Inc.
3) Zar, J. (1996). Biostatistical Analysis. New Jersey: Prentice-Hall International, Inc.
4) Μενεξές, Γ. & Οικονόμου, Α. (2002). Σφάλματα και Παρανοήσεις στους Στατιστικούς Ελέγχους Υποθέσεων: Υπέρβαση μέσω της Ανάλυσης Δεδομένων. Τετράδια Ανάλυσης Δεδομένων-Data Analysis Bulletin, 2, 52-64.
5) Μενεξές, Γ. (2007). Μια Δομημένη Προσέγγιση στην Πολυμεταβλητή Στατιστική Ανάλυση Βιολογικών, Περιβαλλοντικών, Κοινωνικών και Οικονομικών Δεδομένων. Στο Φυσικοί Πόροι, Περιβάλλον και Ανάπτυξη (σσ. 519-534). Επιμέλεια: Γ. Αραμπατζής και Σ. Πολύζος. Θεσσαλονίκη: Εκδόσεις Τζιόλα.
6) Μενεξές, Γ. (2013). Οδηγός Ανάλυσης Παραλλακτικότητας Δεδομένων Γεωργικών Πειραμάτων με Στατιστικά Πακέτα. Εκπαιδευτικές Σημειώσεις.
Last Update
01-09-2024
