Vorlesung

Applied Numerical Computing and Deep Learning in Python

Nummer

011062, WS2627

Dozentinnen und Dozenten

• Privatdozent Dr. Andriy Sokolov

Veranstaltungstyp

Vorlesung, 2+2

Ort und Zeit

M/511 Mi 14:00 2h

Modul-Zugehörigkeit (ohne Gewähr)

DPL:A:-:- – Mathematik für andere Fächer (Service)

DPL:B:-:2 – Mathematik, Diplom (auslaufend)

MABA:-:4:MAT-443

TMABA:-:4:MAT-443

WIMABA:-:4:MAT-443

MAMA:-:4:MAT-443

TMAMA:-:4:MAT-443

WIMAMA:-:4:MAT-443

Sprechstunde zur Veranstaltung

nach Vereinbarung

Erforderliche Voraussetzungen

Students are expected to have a solid understanding in Numerical methods

Inhalt

The course introduces students to programming in Python, numerical methods, and basic concepts of artificial intelligence. Students learn to implement algorithms such as solving linear systems, interpolation, and matrix decompositions in Python. In the final part, these skills are applied to simple deep learning models and practical tasks such as image recognition.

Bemerkungen

Link zum Modulhandbuch Mathematik

Empfohlene Literatur

• Numerical Python: Scientific Computing and Data Science Applications with NumPy, SciPy and Matplotlib (Second Edition)” by Robert Johansson, Apress, 2018.
• Available online: https://jrjohansson.github.io/numericalpython.html
• Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016. Available online: https://www.deeplearningbook.org
• Algorithmic Mathematics in Machine Learning by Bastian Bohn, Jochen Garcke and Michael Griebel, 2024

Übungen

Leiter der Übung

Andriy Sokolov

Nummer der Übung

011063

Übungsgruppen

n. V.