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: 05.09.2023 - 11.12.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Juho Rousu
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
valid for whole curriculum period:
Generalization error analysis and estimation; Model selection; Optimization and computational complexity; Linear models; Support vector machines and kernel methods; Boosting; Feature selection and sparsity; Multi-layer perceptrons; Multi-class classification; Preference learning
Assessment Methods and Criteria
valid for whole curriculum period:
Exercises and course exam
Workload
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
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
4 Quality Education
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I - II
2023-2024 Autumn I - II