LEARNING OUTCOMES
Students can formalize applications as ML problems and solve them using basic ML methods.
Students can perform basic exploratory data analysis.
Students understand the meaning of the train-validate-test approach in machine learning.
Students can apply standard regression and classification models on a given data set.
Students can apply simple clustering and dimensionality reduction techniques on a given data set.
Students are familiar with and can explain the basic concepts of reinforcement learning.
Credits: 5
Schedule: 05.09.2022 - 14.10.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Pekka Marttinen, 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
Content
valid for whole curriculum period:
Exploratory data analysis.
Dimensionality reduction, PCA.
Regression and classification.
Clustering.
Deep learning.
Reinforcement learning.
Assessment Methods and Criteria
valid for whole curriculum period:
Assignments, project report, participation in peer-grading.
Workload
valid for whole curriculum period:
5 credits approx. 134 hours of work divided into
Lectures + self-study: 10*(2+3)=50 hours
Assignments: 6 * 9 = 54 hours
Project work: 26 hours
Peer-grading: 4 hours
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
1 No Poverty
2 Zero Hunger
3 Good Health and Well-being
5 Gender Equality
6 Clean Water and Sanitation
7 Affordable and Clean Energy
8 Decent Work and Economic Growth
9 Industry, Innovation and Infrastructure
10 Reduced Inequality
11 Sustainable Cities and Communities
12 Responsible Production and Consumption
13 Climate Action
14 Life Below Water
15 Life on Land
16 Peace and Justice Strong Institutions
17 Partnerships for the Goals
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I
2023-2024 Autumn I