CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

# Machine learning in Julia programming environment

The course is not on the list Without time-table
Code Completion Credits Range Language
01SUJ KZ 3 1P+2C Czech
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Mathematics
Synopsis:
Requirements:
Syllabus of lectures:

1. Introduction to Julia, advantages over Matlab, Python, R. Variables and operators.

2. Functions, numerical types and multiple dispatch.

3. If-else statements, for and while loops, basic iterators.

4. Composite types and constructors. Parametric types.

5. Modules and environments. Using already existing code.

6. Useful packages: Plots.jl for creating plots, DataFrames.jl for working with tabular data.

7. Optimization. Gradient descent and stepsize selection.

8. Regression and classification. Linear regression and SVM.

9. Neural networks I. Creating a neural network manually and with Flux.jl.

11. Ordinary differential equations. Algorithms. ODE.jl package.

12. Statistics I. Introduction. Limit theorems. Distributions.jl.

13. Statistics II. Maximum likelihood. Uniform distribution on a sphere or a simplex.

Syllabus of tutorials:
Study Objective:
Study materials:

Key references:

[1] Bezanson, Jeff, et al. Julia: A fresh approach to numerical computing. SIAM review 59.1 (2017): 65-98.

[2] Kochenderfer, Mykel J., and Tim A. Wheeler. Algorithms for optimization. MIT Press, 2019.

[3] Lauwens, Ben, and Allen B. Downey. Think Julia: how to think like a computer scientist. O'Reilly Media, 2019.

Recommended references:

[4] Julia Documentation. @ https://docs.julialang.org/en/v1/manual/documentation/index.html

Note:
Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-05-23
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