ELEC-E7261 - Ambient Intelligence D, Lecture, 11.1.2023-5.4.2023

Guest access
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

Understanding of good practices for machine learning with noisy and inaccurate data; feature extraction/ feature subset selection, handling high dimensional data, ANN + Deep Learning, Probabilistic graphical models, Topic models; as well as Unsupervised learning and clustering, Anomaly detection and Recommender systems.

Credits: 8

Schedule: 11.01.2023 - 05.04.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Stephan Sigg

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

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Examination, Assignments and group works

Workload

  • valid for whole curriculum period:

    Contact hrs 26 h
    Independent work 84 h

DETAILS

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III - IV
    2023-2024 Spring III - IV

    Enrollment :

    Registration for Courses on Sisu (sisu.aalto.fi).

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

Aalto University Code of Academic Integrity and Handling Violations Thereof