Machine Learning

Description

Homeworks and code for the labs of the Machine Learning 1 and Machine Learning 2 courses of the MSc in Artificial Intelligence at the University of Amsterdam.

Machine Learning 1

Homeworks

• Probability Theory, Linear Algebra and Matrix Calculus: Statement - Solution
• MAP Solution for Linear Regression, Probability Distributions, Likelihoods, and Estimators: Statement - Solution
• Naive Bayes Classification and Multi-class Logistic Regression: Statement - Solution
• Constrained Optimization and Kernel Outlier Detection: Statement - Solution
• PCA and Expectation Maximization for Mixture Models: Statement - Solution

Labs

Polynomial Regression and Bayesian Regression

Joint work with Mathijs Mul: Assignment and Solutions

Polynomial Regression	Bayesian Regression

Multi-Class Logistic Regression and Multi-Layer Perceptrons

Joint work with Mathijs Mul and Kwesi Menyah: Assignment and Solutions

Learned Weights

Gaussian Process Regression

Joint work with Mathijs Mul and Sebastian Bertoli: Assignment and Solutions

Gaussian Process Regression

Machine Learning 2

Homeworks

• Probability Distributions, Multivariate Gaussians and Exponential Family: Solution
• D-Separation and Factor Graphs: Solution
• Expectation Maximization: Solution
• Sampling Methods and Variational EM: Solution
• Linear Systems and Causality: Solution

Labs

Check my joint repository with Dana Kianfar.

Testing

Refer to each lab and run the iPython notebook as follows.

jupyter notebook $notebook_name$.ipynb

Dependencies

• iPython notebook
• SciPy
• NumPy
• Matplotlib

Copyright

Copyright © 2016-2017 Jose Gallego.

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