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: 1 - 8
Schedule: 10.01.2024 - 10.04.2024
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
Independent work
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:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III - IV
2023-2024 Spring III - IVEnrollment :
Registration for Courses on Sisu (sisu.aalto.fi).