Applied Linear Algebra for Data Science
Course, Master's level, 1TD060
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 March 2024–2 June 2024
- Language of instruction
- English
- Entry requirements
-
120 credits. Computer Programming II or Programming, Bridging Course. Linear Algebra II. One of Introduction to Scientific Computing, Scientific Computing II, Scientific Computing Bridging Course or Statistical Machine Learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 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.
- Application fee: SEK 900
- First tuition fee instalment: SEK 18,125
- Total tuition fee: SEK 18,125
- Application deadline
- 16 October 2023
- Application code
- UU-62030
Admitted or on the waiting list?
- Registration period
- 4 March 2024–25 March 2024
- Information on registration
Spring 2024 Spring 2024, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 March 2024–2 June 2024
- Language of instruction
- English
- Entry requirements
-
120 credits. Computer Programming II or Programming, Bridging Course. Linear Algebra II. One of Introduction to Scientific Computing, Scientific Computing II, Scientific Computing Bridging Course or Statistical Machine Learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 4 March 2024–25 March 2024
- Information on registration
About the course
The fields of data science and machine learning lean on many applications of linear algebra. Data are often represented in matrix form, and data are analysed through matrix and vector operations. If you would like to understand the relations between features, meaning understanding how columns depend on each other, it can be done with the algorithm QR factorisation.
To reduce the dimensionality of large data sets (transform a large set of variables into a smaller one that still contains most of the information) is in the data science world called Principal Component Analysis (PCA), but is really the same thing as Singular Value Decomposition. Again, it is really an algorithm in linear algebra. Ranking algorithms, like the Pagerank algorithm which formed the basis of Google, are really a form of an algorithm for finding eigenvalues. To really understand data science and machine learning, linear algebra is essential.
In this course, we focus on numerical linear algebra, i.e. the computational methods and algorithms used in data science. We look at how data is stored, how computations are performed efficiently, and why the methods work.