Machine learning

tags: Artificial Intelligence, Applied maths

Machine learning is about constructing algorithms that can approximate complex functions from observations of input/output pairs. Machine learning is related to Statistics since its goal is to make predictions based on data.

Examples of such functions include:

Image classification
Time-series prediction
Language modeling

Regression

The goal is to approximate a target function \(f\) or signal \(S\).

Links to this note

Adversarial examples
Alternative learning mechanisms
Automated theorem proving
Catastrophic forgetting
Computer vision
Continual learning
Data representation
Distillation
Federated learning
Gaussian Processes
Generalization in Machine learning
Generative modelling
Homomorphic encryption
Implicit neural representations
Kernel Methods
Learning in dynamical systems
Meta-learning
Neural network training
Neural networks
NLP
Notes on: Generalization over different cellular automata rules learned by a deep feed-forward neural network by Aach, M., Goebbert, J. H., & Jitsev, J. (2021)
Privacy-preserving machine learning
Recurrent neural networks
Reinforcement learning
Reservoir computing
Self-supervised learning
Softmax
Supervised learning
Talk: Artificial Intelligence: A Guide for Thinking Humans
Talk: Differentiation of black-box combinatorial solvers
The Bitter Lesson
Transfer learning
Unsupervised learning
Why programming is a good medium for expressing poorly understood and sloppily-formulated ideas

Last changed 29/08/2021 | authored by Hugo Cisneros

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