Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
After the course, the student knows how to recognize and formalize supervised machine learning problems, how to implement basic optimization algorithms for supervised learning problems, how to evaluate the performance supervised machine learning models, and has understanding of the statistical and computational limits of supervised machine learning, as well as the principles behind commonly used machine learning models.
Credits: 5
Schedule: 03.09.2024 - 10.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Juho Rousu, Jaakko Hollmen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
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valid for whole curriculum period:
Generalization error analysis and estimation; Model selection; Optimization and computational complexity; Linear models; Support vector machines and kernel methods; Ensemble methods; Feature selection and sparsity; Multi-layer perceptrons; Multi-class classification; Preference learning
Assessment Methods and Criteria
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valid for whole curriculum period:
Exercises and course exam
Workload
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valid for whole curriculum period:
Workload: 24 lecture hours, 12 hours exercise session, 3 hours exam, 96 hours independent study
Attendance in lectures and exercise sessions is voluntary.
Course can be completed online.
DETAILS
Study Material
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valid for whole curriculum period:
Lecture slides and exercises.
Supplementary reading:
- Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning
- Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press
Substitutes for Courses
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valid for whole curriculum period:
Prerequisites
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valid for whole curriculum period:
SDG: Sustainable Development Goals
4 Quality Education
FURTHER INFORMATION
Further Information
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valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - II