Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Introduktion till maskininlärning |
|---|---|
| English title | Introduction to Machine Learning |
| Course number | 5650 |
| Credits | 1.5 |
| Responsible KI department | Institutet för miljömedicin |
| Specific entry requirements | Epidemiology I and Biostatistics I or corresponding courses where basic statistical concepts and linear regression are introduced. |
| Grading | Passed /Not passed |
| Established by | The Committee for Doctoral Education |
| Established | 2022-09-19 |
| Purpose of the course | 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). |
| Intended learning outcomes | 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. |
| Contents of the course | 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. |
| Teaching and learning activities | Lectures, quizzes, group sessions and computer labs. |
| Compulsory elements | 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. |
| Literature and other teaching material | Suggested reading:
An introduction to statistical learning by James, Witten, Hastie and Tibshirani. https://www.statlearning.com |
Course responsible |
Anita Berglund Institutet för miljömedicin Anita.Berglund@ki.se |
| Contact person |
Johanna Bergman Institutet för miljömedicin johanna.bergman@ki.se Nobels väg 13 17177 Stockholm |
