MACHINE LEARNING

After completing the course on Machine learning, you will be:

• Able to explain the manifestation of machine learning and its possible applications, and be familiar with several concepts like data modeling, over fitting, under fitting, generalisation, memorisation, learning data, and validating data.
• Aware of supervised learning algorithms and their different kinds and applications
• Able to apply different regression methods as well as neural networks to capture hidden relations in supervised learning
• Able to explain classification algorithms as well as probabilistic models and Bayesian-based machine learning algorithms and apply them in simple scenarios
• Aware of unsupervised learning concepts and clustering, data quality in machine learning and how to improve and clean data. Aware of the applications as well as limitations of machine learning algorithms.
• Able to define reinforcement learning and its main differences between supervised and unsupervised machine learning.

In this course, the different types of machine learning are covered as well as the main concepts. The approach of this course is to approach machine learning from an algorithmic perspective. The aim of this approach is to understand the theories/algorithms behind machine learning algorithms and how to choose the best one for our specific problem, to know its limits and even how to modify it to fit our specific problem.

Timetable:

*Time zone: Europe/Helsinki*

14 Jan 2025 12.00 – 14.00

16 Jan 2025 12.00 – 14.00

21 Jan 2025 12.00 – 14.00

23 Jan 2025 12.00 – 14.00

27 Jan 2025 12.00 – 14.00

29 Jan 2025 12.00 – 14.00

5 Feb 2025 12.00 – 14.00

6 Feb 2025 12.00 – 14.00

10 Feb 2025 12.00 – 14.00

12 Feb 2025 14.00 – 16.00

17 Feb 2025 12.00 – 14.00

19 Feb 2025 14.00 – 16.00

24 Feb 2025 12.00 – 14.00

25 Feb 2025 12.00 – 14.00

3 Mar 2025 12.00 – 14.00

5 Mar 2025 14.00 – 16.00

• EUNICE student: enrolled as a student in one of the universities of EUNICE European University consortium (check universities here).
• B2 level of English.
• It is recommended to know: the fundamentals of probability theory, linear algebra, optimisation theory, matrix calculus, and some programming skills.

Study Level: Master

• Submit your application via the button ‘Apply Now’.
• Please keep in mind that the number of participants could be limited for each course. Application does not guarantee enrolment in the course.
• The course participants will be selected based on criteria specified in the study guide.
• Your home university will inform you whether you have been accepted and provide further information about the next steps.

Specific instructions in some universities:

• BTU applicants: for questions about enrolment and recognition at your university, you can visit this website
• UPHF applicants: make sure to ask the approval of your director of studies (responsable pédagogique) before applying. For any question, you can contact the EUNICE office: eunice@uphf.fr
• UoP applicants: questions about enrolment and recognition can be answered by your Director of Studies or ECTS Coordinator, or you can contact eunice@go.uop.gr

Any questions about enrolment or credit recognition? Contact your EUNICE courses coordinator.