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docs(ml): add Mamba paper to machine_learning index (#893)

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Add "Mamba: Linear-Time Sequence Modeling with Selective State Spaces"
by Albert Gu and Tri Dao (2023) — a state-space model with linear-time
sequence processing that rivals Transformer performance while enabling
extremely long context windows with lower compute.
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This paper introduces AlexNet, a neural network architecture which dramatically improved over the state-of-the-art in image classification algorithms and is widely regarded as a breakthrough moment for deep learning.
* [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/pdf/2312.00752.pdf) by Albert Gu and Tri Dao
This paper introduces Mamba, a state-space model with linear-time sequence processing that rivals Transformer performance while enabling extremely long context windows with lower compute — challenging the dominance of attention-based architectures for sequence modeling.
* [Interpretable machine learning: definitions, methods, and applications](https://arxiv.org/pdf/1901.04592.pdf)
This paper introduces the foundations of the rapidly emerging field of interpretable machine learning.