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: 10.01.2022 - 08.04.2022
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Alex Jung, 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:
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 for whole curriculum period:
assignments, project report
Workload
valid for whole curriculum period:
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 for whole curriculum period:
see course page
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:
CS-C3240 Machine Learning overlaps with CS-E3210 Machine Learning: Basic Principles and only one of them can be included in the degrees. If you have already taken the basic machine learning course, you should take the course CS-E4710 Machine Learning: supervised methods instead.
Teaching Period:
2020-2021 Spring III-IV
2021-2022 Spring III-IV
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CS-C3240
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.
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