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Machine learning

Individual course

Course content:

  • Components of Machine Learning: Data, Hypothesis Space and Loss Functions
  • Algorithms for Machine Learning: Gradient Descent, Greedy Search, Linear Solvers

After completing the course, the students

  • can formalize applications as ML problems and solve them using basic ML methods.
  • understand the concept of generalization and how to analyse it using simple probabilistic models.
  • are familiar with linear models for regression and classification.
  • know how basic ML methods are obtained as combinations of particular choices for data representation (features), hypothesis space (model) and loss function.
  • are familiar with the idea of hard and soft clustering methods.
  • understand the basic idea of feature learning methods.

Workload:

5 credits, approx 130 hours of work divided into:

  • lectures + self-study (30 hours)
  • assignments (6 * 10 = 60 hours)
  • project work (around 40 hours)

For Aalto degree students: This course overlaps with Machine Learning: Basic Principles (CS-E3210) and Machine Learning with Python (CS-EJ3211) and only one of them can be included in the degrees. If you have already taken one of the basic machine learning courses, you should take the course Machine Learning: supervised methods (CS-E4710) instead.

Further information about the courses and studying

Aalto University
FITech ICT contact person
fitech-sci(at)aalto.fi

Responsible teachers

Aalto University
Alex Jung , Assistant professor
alex.jung(at)aalto.fi
Aalto University
Stephan Sigg , Associate professor
stephan.sigg(at)aalto.fi

Contact person for applications

FITech
Pilvi Lempiäinen , Head of study services
pilvi.lempiainen(at)fitech.io
Start here
Start here
Category:
ICT Studies
Topic:
AI and machine learning
Course code:
CS-C3240
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
11.1.–9.4.2021
Application deadline:
3.1.2021
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Online
Language:
English
General prerequisites:
Matrix algebra, probability theory, basic programming skills
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