CS-E4715 - Supervised Machine Learning D, Lecture, 3.9.2024-10.12.2024

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

  • 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

  • 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

Study Material

  • 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
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof