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: 14.09.2021 - 20.12.2021

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; Ranking; Multi-output learning

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Exercises and course exam

DETAILS

Study Material
  • valid for whole curriculum period:

    Lecture slides and exercises.

    Supplementary reading:

    • Shalev-Shwartz, Ben-David: Understanding Machine Learning, Cambridge University Press
    • Mohri, Rostamizadeh, Talwakar: Foundations of Machine Learning

     

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Period:

    2020-2021 Autumn I-II

    2021-2022 Autumn I-II

    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CS-E4710

    Registration for Courses: Kurssille ilmoittaudutaan WebOodissa. Katso ilmoittautumisaika WebOodista. Ilmoittautumiseen