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Introduction to machine learning

Individual course

Max amount of FITech students: 15 (the course is full)

Persons without a valid study right to a Finnish university have preference to this course.

Learn the basics of machine learning theory and learn to apply it to real-life problems!

The course has a short prerequisite knowledge test – available on the course website – which contains a more detailed description of the required prerequisites and pointers to self-study materials.

Course contents

  • Ingredients of machine learning: components (tasks, computational problems, algorithms etc.) and necessary tools.
  • Introduction to statistical learning and probabilistic modelling.
  • Supervised learning: basic definition, basic regression and classification algorithms (linear probabilistic, distance based models).
  • Statistics and evaluation: estimating parameters and resampling methods (including validation set methods).
  • Unsupervised learning: clustering methods (k-means, agglomerative clustering) and basics of dimensionality reduction (PCA and variants).

Learning objectives

Machine learning is the core technology under the recent developments of artificial intelligence (AI), and it is applied widely in several domains. This course will provide the necessary theoretical background to understand the fundamental machine learning concepts and properly use the basic methods of supervised and unsupervised learning to solve real-life problems. The course will prepare you for further studies in machine learning and introduce you to the methods and tools used to solve the problems in practice.

After the course, the student

  • will have the necessary theoretical background to understand and explain the fundamental machine learning principles and concepts (e.g., training data, feature, model selection, loss function, training error, test error, and overfitting)
  • recognises various ingredients in the machine learning tasks (task, computational problems, models, algorithms etc.)
  • can map a practical data analysis problem into a machine learning task, take the correct steps to solve the task, and know how to interpret and evaluate the outcomes
  • understands the underlying assumptions and limitations of the machine learning solution
  • is familiar with the essential tools and programming environments suitable for solving machine learning problems, and you can independently do the basic data analysis tasks with such programming environments
  • understands the concept of generalisation, can use validation set methods and can evaluate the performance of machine learning methods and selection models
  • can read machine learning literature (textbooks, scientific articles etc.) and are prepared for further studies in machine learning or other disciplines that need machine learning methods
  • can explain and report your machine learning approaches and solutions to your peers and future colleagues understandably and coherently

The student also knows the principles of and can apply to real-world problems the following techniques:

  • Supervised learning: basic regression methods (linear etc.), classification methods (at least one example of linear, distance-based, generative, discriminative, and algorithmic)
  • Unsupervised learning: the essential clustering formalisms (k-means, hierarchical clustering) and the most important dimensionality reduction approaches (PCA, at least one distance-based, at least one manifold method).

Course material

  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer, 2017.
  • Additional readings are announced during the course.

Parts of the textbook that are required are specified on the course webpage.

Completion methods

Contact teaching (possible attendance requirements are specified each year on the course webpage)

  • Term project
  • Lectures Wed 12-14, Fri 10-12 (material will be available after the lectures)
  • Exercises Mon 14-16, MO 16-18 and Wed 14-16

This course can be arranged in a different format and with varying methods of assessment, which will be announced on the course web page.

More information on the University of Helsinki’s course page.

You can get a digital badge after completing this course.

Responsible teacher

University of Helsinki
Kai Puolamäki

Further information about the course and studying

University of Helsinki
Reijo Siven

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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5G technology,
AI and machine learning,
Smart systems
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University of Helsinki
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Adult learner,
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General prerequisites:
Generic skills learned during BSc studies, including writing of academic reports. High school and university mathematics, including basics of optimization with differentiation. Linear algebra, including basic matrix and vector operations, eigenvalues, and eigenvectors. Probability and statistics, including random variables, expectation, and rules of probability. Programming skills, some programming experience, and ability to quickly acquire the basics of a new environment such as R or Python. Additionally, it is useful to know the basic ideas of pseudocode and the analysis of time and space complexity with big O notation.
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