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Programming Exercises Accompanying the Lecture "Artificial Intelligence for Robotics"

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Artificial Intelligence for Robotics

A python cheat sheet containing the basics needed for this course can be found here: https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf

The documentation of SciPy can be found here: http://scipy-cookbook.readthedocs.io/index.html

Exercise overview

  • 0_1_python_introduction_exercise: basic python examples
  • 0_2_python_intro_applications: python applications (linear regression, optimization)
  • 1_0_probability_ml_basics: Probability recap and machine learning basics
  • 2_0_regression_pgm: Regression and probabilistic graphical models
  • 3_0_pgo_icp: Pose graph optimization and iterative closest point
  • 4_0_pca_kmeans_svm: Principal Component Analysis (PCA), k-means clustering, Support Vector Machine (SVM).
  • 5_deep_learning: Backpropagation, Convolutional Neural Networks (CNNs), Deep Reinforcement Learning (RL).

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  • Python 67.7%
  • Jupyter Notebook 32.0%
  • Makefile 0.3%