Kursplansdatabas för forskarutbildningskurser
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Kursplansdatabas för forskarutbildningskurser
KURSPLANER FÖR FORSKARUTBILDNINGSKURSER
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Svensk benämning | Introduktion till maskininlärning |
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Engelsk benämning | Introduction to Machine Learning |
Kursnummer | 5650 |
Antal högskolepoäng | 1.5 |
Kursansvarig institution | Institutet för miljömedicin |
Särskild behörighet | Epidemiology I and Biostatistics I or corresponding courses where basic statistical concepts and linear regression are introduced. |
Betygsskala | Godkänd/Icke godkänd |
Fastställd av | The Committee for Doctoral Education |
Datum för fastställande | 2022-09-19 |
Kursens syfte | The purpose of this course is to give an introduction to machine learning without heavy-mathematics. A main focus is on machine learning algorithms for regression analyses using large datasets, both in terms of the number of variables observed and/or the number of units (sample size). |
Kursens lärandemål | After successfully completing this course, the student is expected to be able to:
- Recognize and formulate well defined questions that can be solved using machine learning algorithms: both prediction and inference questions. - Explain key concepts in machine learning, including curse of dimensionality, out of sample validation, generalization, uncertainty. - Choose relevant machine learning algorithms for prediction and inference. - Conduct and interpret simple analyses using machine learning algorithms on real data. |
Kursens innehåll | This course focuses on machine learning algorithms for regression analyses using large datasets, both in terms of the number of variables observed and/or the number of units (sample size). Register data studies are a typical example where such large datasets are analysed. The course will start by going through key concepts of statistical learning, including regression and prediction problems, curse of dimensionality, out of sample validation, generalization, uncertainty. These are problems and concepts that students need to be able to recognize, formulate and explain. The course will then present some central machine learning algorithms, including Lasso, trees, random forest, bagging, together with methods to validate the algorithms and to draw inference. The students will in particular learn how to choose relevant algorithms for specific situations. |
Arbetsformer | Lectures, quizzes, group sessions and computer labs. |
Obligatoriska moment | Individual examination (summative assessment). |
Examination | To pass the course, the student must show that the learning outcomes have been achieved. Assessments methods used are group tasks (formative assessments) along with a written individual task (summative assessment). The examination is viewed as a contribution to the development of knowledge, rather than as a test of knowledge. Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. |
Kurslitteratur och övriga läromedel | Suggested reading:
An introduction to statistical learning by James, Witten, Hastie and Tibshirani. https://www.statlearning.com |
Kursansvarig |
Anita Berglund Institutet för miljömedicin Anita.Berglund@ki.se |
Kontaktpersoner |
Johanna Bergman Institutet för miljömedicin johanna.bergman@ki.se Nobels väg 13 17177 Stockholm |