# Foundations and Trends® in Machine Learning

**Type:** Journals  
**Canonical URL:** https://scholariq.org/journals/foundations-and-trends-in-machine-learning/

## Facts

| Field | Value |
| --- | --- |
| Citations | 46,483 |
| h-index | 45 |
| Homepage | http://www.nowpublishers.com/product.aspx?product=MAL |
| Open Access | false |
| ISSN-L | 1935-8237 |
| ISSNs | 1935-8237,1935-8245 |
| OpenAlex ID | https://openalex.org/S4210188176 |
| Publisher | Now Publishers |
| Works | 77 |

## Journal top topics

Showing 8 of 25.

- [Random Matrices and Applications](https://scholariq.org/topics/random-matrices-and-applications/)
- [Tensor decomposition and applications](https://scholariq.org/topics/tensor-decomposition-and-applications/)
- [Reinforcement Learning in Robotics](https://scholariq.org/topics/reinforcement-learning-in-robotics/)
- [Neural Networks and Applications](https://scholariq.org/topics/neural-networks-and-applications/)
- [Machine Learning and Algorithms](https://scholariq.org/topics/machine-learning-and-algorithms/)
- [Gaussian Processes and Bayesian Inference](https://scholariq.org/topics/gaussian-processes-and-bayesian-inference/)
- [Bayesian Methods and Mixture Models](https://scholariq.org/topics/bayesian-methods-and-mixture-models/)
- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.