Machine learning

The course addresses components of machine learning such as data, hypothesis space and loss functions and it also introduces algorithms for machine learning: gradient descent, greedy search and linear solvers.
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Schedule:

Teaching time:

Daytime

Location:

Online

Topic:

Information and communications technology

Form of learning:

Online

Provider:

Aalto University, FITech

Level:

Intermediate

Credits:

5 By Aalto University (ECTS)

Fee:

Free of charge

Target group and prerequisites

Matrix algebra, probability theory, basic programming skills.

Course description

Course contents

  • Components of machine learning: Data, hypothesis space and loss functions
  • Algorithms for machine learning: gradient descent, greedy search, linear solvers

Learning outcomes

After completing the course, the students

  • can formalise applications as ML problems and solve them using basic ML methods.
  • understand the concept of generalisation 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.

Teaching methods

The course follows a schedule and includes lectures, self study, assignments, and a project work.

The lectures might be organised on campus but will in any case be available online.

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)

Application deadline: 2.1.2022.

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