Modulbeschreibung

Artificial Intelligence for 3D Model Detection

ECTS-Punkte:
15
Lernziele:
  • Understanding of 3D model data structures and its design processes.
  • Know how to analyze and deconstruct 3D models that will be used as inputs for machine learning.
  • Ability to implement neural networks using existing frameworks to recognize 3D models.
  • Ability to evaluate and compare different algorithms and models with regard to efficiency and effectivity.

Kurse in diesem Modul

Stochastic Modelling:
  • Probability review: random variables, conditional probabilities, theorem of large numbers, central limit theorem.
  • Introduction to discrete and continuous stochastic processes.
  • Discrete, continuous and hidden Markov Chains.
  • Bernoulli, Poisson, Gaussian Processes, Brownian motion, white and coloured noise.
Seminar mit undefined Lektionen pro Woche
Artificial Intelligence for 3D Model Detection:
  • Digital image fundamentals and 3D model data structures.
  • Deep learning: Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks.
  • Implementation of deep learning strategy using deep learning frameworks, in this case: TensorFlow.
  • Evaluation and comparison between models and architectures.
Projekt mit undefined Lektionen pro Woche
Disclaimer

Diese Beschreibung ist rechtlich nicht verbindlich! Weitere Informationen finden Sie in der detaillierten Modulbeschreibung.