Machine Learning
Course, Master's level, 2ST129
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 November 2023–14 January 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7.5 credits programming in R, Python or Julia.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,500
- Total tuition fee: SEK 12,500
- Application deadline
- 17 April 2023
- Application code
- UU-26605
Admitted or on the waiting list?
- Registration period
- 27 July 2023–27 August 2023
- Information on registration from the department
Autumn 2024 Autumn 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 6 November 2024–19 January 2025
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science. 7.5 credits programming in R, Python or Julia.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,500
- Total tuition fee: SEK 12,500
- Application deadline
- 15 April 2024
- Application code
- UU-26605
Admitted or on the waiting list?
- Registration period
- 25 July 2024–1 September 2024
- Information on registration from the department
About the course
The course is a broad introduction to machine learning (ML) and covers supervised, unsupervised, and reinforcement learning. The course covers core ideas in ML, such as training, validation, and test of predictive models, cross-validation, (stochastic) gradient descent, ensembles, (convolutional, feed-forward, and transformer) neural networks, probabilistic mixtures, (variational) autoencoders, and bandits. The subjects are studied both theoretically, and practically in computer assignments and through an applied ML project.
Reading list
No reading list found.