Course

DAT221 Fundamentals for data science

Credits (ECTS):5

Course responsible:Alexander Johannes Stasik

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Course frequency:Annually

Nominal workload:125 hours: Lectures 13x2 hours = 26 hours, exercises under supervision 13x4 hours = 52 hours, independent study 47 hours

Teaching and exam period:This course starts in the August block. This course has teaching and evaluation during the August block.

About this course

The course covers:

- Recap of linear algebra and analysis

- Matrix decompositions (eigendecomposition, SVD, Cholesky, low-rank approximations, etc.)

- Vector calculus (gradients of vector-valued functions, backpropagation and automatic differentiation, Taylor series, etc.)

- Probability and distributions (probability spaces, Bayes’ theorem, Gaussian distributions, change of variables, exponential family)

-Continuous optimisation (gradient descent, constrained optimisation, convex optimisation)

Learning outcome

This course provides an overview of mathematical methods and tools relevant to data science applications and equips students with the necessary background for advanced courses in machine learning and statistics.
  • Learning activities
    Daily lectures and complementary exercises.
  • Teaching support
    Supervised exercises.
  • Prerequisites
    None
  • Recommended prerequisites
    Linear algebra and analysis knowledge, MATH121, MATH122 and MATH123 or equivalent
  • Assessment method
    Portfolio assessment. Active participation and sufficient hand in exercises each week. Attendance at lectures and exercise sessions on at least 10 out of 14 days. Pass/fail.

  • Teaching hours
    • Lectures: 30 hours
    • Exercise sessions: 45 hours
  • Preferential right
    Master in Data Science
  • Admission requirements
    REALFAG