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
Students can formalize applications as ML problems and solve them using basic ML methods.
Students understand the concept of generalization and how to analyse it using simple probabilistic models.
Students are familiar with linear models for regression and classification.
Students know how basic ML methods are obtained as combinations of particular choices for data representation (features), hypothesis space (model) and loss function.
Students are familiar with the idea of hard and soft clustering methods.
Students understand the basic idea of feature learning methods
Credits: 5
Schedule: 11.01.2021 - 26.03.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Alex Jung, Stephan Sigg
Teacher in charge (applies in this implementation): Alex Jung, Stephan Sigg
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
Components of Machine Learning: Data, Hypothesis Space and Loss Functios
Algorithms for Machine Learning: Gradient Descent, Greedy Search, Linear Solvers
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
assignments, project report
Workload
Valid 01.08.2020-31.07.2022:
5 credits approx 130 hours of work divided into
lectures + self-study (30 hours)
assignments (6 * 10 = 60 hours)
project work (around 40 hours)
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
see course page
Prerequisites
Valid 01.08.2020-31.07.2022:
Matrix Algebra, Probability Theory, Basic Programming Skills
MS-A0111 - Differential and integral calculus 1,
MS-A0011 - Matrix Algebra
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
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