User:Sebastian: Difference between revisions
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*[[Anomaly detection]] | * [[Agglomerative clustering]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g]) | ||
* [[ | * [[Anomaly detection]] | ||
* [[ | * [[Artificial intelligence]] | ||
* [[Bayesian linear regression]] | |||
* [[Bit]] | |||
* [[Byte]] | |||
* [[Classification]] | |||
* [[Data mining]] | * [[Data mining]] | ||
* [[Evaluation metrics]] | * [[Evaluation metrics]] | ||
* [[ | * [[Expert system]] | ||
* [[Unsupervised learning]] ([[clustering]], [[dimensionality reduction]], [[recommender system]]s, [[deep learning]], [[Density estimation]], [[Market basket analysis]]) | * [[Unsupervised learning]] ([[clustering]], [[dimensionality reduction]], [[recommender system]]s, [[deep learning]], [[Density estimation]], [[Market basket analysis]]) | ||
* [[Semi-supervised learning]] | * [[Semi-supervised learning]] | ||
* [[Reinforcement learning]] | * [[Reinforcement learning]] | ||
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* [[Stepwise regression]] | * [[Stepwise regression]] | ||
* [[Ridge regression]] | * [[Ridge regression]] | ||
* [[Neural network regression]] | * [[Neural network regression]] | ||
* [[Decision forest regression]] | * [[Decision forest regression]] | ||
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* [[Procgen Benchmark]] | * [[Procgen Benchmark]] | ||
* [[Optimization]] | * [[Optimization]] | ||
* [[Silhouette score]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g]) | |||
* [[Soft computing]] | |||
* [[K-nearest neighbor]] | |||
* [[NumPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa] | * [[NumPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa] | ||
Revision as of 03:33, 9 May 2022
Vipul: "ok, so for ML wiki, I think you should pick some page or pages to write fully (i.e., long pages) and discuss with @Issa and me as you're doing it, so we can thnink through the right structure of the pages Vipul In parallel, you can continue the process of creating small, stub pages as you learn things Vipul Vipul Naik That's the T-shaped idea: do a few things deeply and then a lot of things (wide) and later you can deepen those other things."
All existing pages here[1]
Red links:
- Agglomerative clustering ([2])
- Anomaly detection
- Artificial intelligence
- Bayesian linear regression
- Bit
- Byte
- Classification
- Data mining
- Evaluation metrics
- Expert system
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning, Density estimation, Market basket analysis)
- Semi-supervised learning
- Reinforcement learning
- Machine learning applications
- Machine learning algorithms
- Simple regression (Simple linear regression, Simple non-linear regression)
- Multiple regression
- Ordinal regression
- Poisson regression
- Fast forest quantile regression
- Linear regression (expand)
- Polynomial regression
- Lasso regression
- Stepwise regression
- Ridge regression