Labs and Homeworks for Machine Learning course, MSc AI @ UvA 2018/2019.

Solutions and implementation from Davide Belli and Gabriele Cesa.

Lab 1: Independent Component Analysis

Topics:

• ICA on mixtures of audio files

Lab 2: Inference in Graphical Models

Topics:

• The sum-product algorithm
• The max-sum algorithm
• Medical Graph

Lab 3: Expectation Maximization and Variational Autoencoder

Topics:

• Expectation Maximization
• Variational Auto-Encoder

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Homework 1: Exponential Families

Topics:

• Probabilities,
• MLE and MAP solutions
• Expectation, mean and covariance
• Exponential families and conjugate priors

Homework 2: Information Theory and Graphical Models

Topics:

• Mutual Information, KL-divergence, entropy
• Directed Graphs, Bayesian Networks, Markov Blankets

Homework 3: ICA and Markov Chains

Topics:

• Conditional entropy and MI
• ICA
• Markov Chains and d-separation

Homework 4: Message passing

Topics:

• Factor Graphs of BN
• Sum-Product algorithm

Homework 5: Generative Models

Topics:

• Gaussian Mixture Models
• EM algorithm
• Mixtures of Bernoulli

Homework 6: Sampling, Variational EM

Topics:

• Rejection, Importance, Independence, Gibbs sampling
• Variational EM on Mixtures of multivariate Bernoulli
• Random walks

Homework 7: LDS and Causal Networks

Topics:

• VEM on Linear Dynamical Systems
• Causal Bayesian Networks and Simpsons's Paradox
• Structural Causal Models and Truncated Factorization

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Copyright

Copyright © 2019 Davide Belli.

This project is distributed under the MIT license. Please follow the UvA regulations governing Fraud and Plagiarism in case you are a student.