FYSS5120 Efficient Numerical Programming (4 cr)

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Study level:
Advanced studies
Grading scale:
Pass - fail
Language:
English, Finnish
Responsible organisation:
Department of Physics
Curriculum periods:
2024-2025, 2025-2026, 2026-2027, 2027-2028

Description

  • Python and Julia programming languages

  • Keras and TensorFlow libraries in machine learning

  • NumPy, SciPy, and Numba libraries in numerics

  • Parallel programming and GPU computation

Learning outcomes

At the end of the course students will be able to

  • write numerical code in Python using NumPy, SciPy and Numba libraries

  • use Keras and Tensorflow libraries in machine learning
  • make regression analysis using gaussian processes

  • write parallel code for multicore prosessors

  • use MPI parallelization in computer clusters using Python

  • write basic Julia code and knows how it differs from Python

  • write a simple GPU code in Julia

Description of prerequisites

Programming experience with Python, C++ or some other programming language.

Completion methods

Method 1

Evaluation criteria:
Accepted solutions to programming assignments.
Time of teaching:
Period 1
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Parts of the completion methods
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Teaching (4 cr)

Type:
Participation in teaching
Grading scale:
Pass - fail
Language:
English

Teaching