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: 5
Schedule: 12.01.2022 - 13.04.2022
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
Study Material
valid for whole curriculum period:
Lecture handouts/slides,
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
4 Quality Education
9 Industry, Innovation and Infrastructure
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2020-2021 Spring III-IV
2021-2022 Spring III-IV
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=ELEC-E7260
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
WebOodi