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Machine Learning

License

Description

Homeworks and labs of the Machine Learning 1 and Machine Learning 2 courses of the MSc in Artificial Intelligence at the University of Amsterdam. Homeworks are individual, while labs are joint work with Gabriele Bani.

Machine Learning 1

Homeworks

Labs

Polynomial Regression and Bayesian Regression

Problem statement and Solution

Polynomial Regression Bayesian Regression

Multi-Class Logistic Regression and Multilayer Perceptrons

Problem statement and Solution

Learned Weights for single-layer MLP Learned Weights for Logistic Regression

Gaussian Processes and Support Vector Machines

Problem statement and Solution


Gaussian Process Regression


SVM Classification

Machine Learning 2

Homeworks

Labs

Independent Component Analysis

Problem statement and Solution

Original Signals Reconstructed Signals

Inference in Graphical Models

Problem statement and Solution


Factor Graph of Interest

Expectation Maximization and Variational Autoencoder

Problem statement and Solution


Manifold Learned by VAE on MNIST

Testing

Refer to each notebook name and run Jupyter notebook with a following command:

jupyter notebook #notebook#.ipynb

Dependencies

  • Numpy
  • Matplotlib
  • Scipy
  • IPython Notebook

Copyright

Copyright © 2017-2018 Andrii Skliar.

This project is distributed under the MIT license. This was developed as part of the courses Machine Learning 1 and Machine Learning 2 taught by Rianne van den Berg, Max Welling, Patrick Forré and Joris Mooij at the University of Amsterdam. Please follow the UvA regulations governing Fraud and Plagiarism in case you are a student.