@article{harris_array_2020,
	title = {Array programming with {NumPy}},
	volume = {585},
	url = {https://doi.org/10.1038/s41586-020-2649-2},
	doi = {10.1038/s41586-020-2649-2},
	pages = {357--362},
	number = {7825},
	journaltitle = {Nature},
	author = {Harris, Charles R. and Millman, K. Jarrod and Walt, Stéfan J. van der and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and Kerkwijk, Marten H. van and Brett, Matthew and Haldane, Allan and Río, Jaime Fernández del and Wiebe, Mark and Peterson, Pearu and Gérard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
	date = {2020-09},
	year = {2020},
	note = {Publisher: Springer Science and Business Media {LLC}},
}

@article{hunter_matplotlib_2007,
	title = {Matplotlib: A 2D graphics environment},
	volume = {9},
	doi = {10.1109/MCSE.2007.55},
	abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.},
	pages = {90--95},
	number = {3},
	journaltitle = {Computing in Science \& Engineering},
	author = {Hunter, J. D.},
	date = {2007},
	year = {2007},
	note = {Publisher: {IEEE} {COMPUTER} {SOC}},
}

@article{pedregosa_scikit-learn_2011,
	title = {Scikit-learn: Machine Learning in Python},
	volume = {12},
	pages = {2825--2830},
	journaltitle = {Journal of Machine Learning Research},
	author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
	date = {2011},
	year = {2011},
}

@inreference{noauthor_stochastic_2025,
	title = {Stochastic gradient descent},
	rights = {Creative Commons Attribution-{ShareAlike} License},
	url = {https://en.wikipedia.org/w/index.php?title=Stochastic_gradient_descent&oldid=1309164477},
	abstract = {Stochastic gradient descent (often abbreviated {SGD}) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the very high computational burden, achieving faster iterations in exchange for a lower convergence rate.
The basic idea behind stochastic approximation can be traced back to the Robbins–Monro algorithm of the 1950s. Today, stochastic gradient descent has become an important optimization method in machine learning.},
	booktitle = {Wikipedia},
	urldate = {2025-09-22},
	date = {2025-09-02},
	year = {2025},
	langid = {english},
	note = {Page Version {ID}: 1309164477},
	file = {Snapshot:/home/lars/Zotero/storage/B6CVR59B/index.html:text/html},
}

@unpublished{elstner_lecture_2025,
	location = {Karlsruhe Institute for Technology, Karlsruhe},
	title = {Lecture: Machine Learning for Chemistry},
	type = {Lecture},
	howpublished = {Lecture},
	author = {Elstner, Marcus and Kubar, Tomas},
	date = {2025-05-20},
	year = {2025},
	langid = {german},
	file = {PDF:/home/lars/Zotero/storage/5LSLJMK8/Elstner and Kubar - 2025 - Lecture Machine Learning for Chemistry.pdf:application/pdf},
}

@online{lekhansh_lasso_2024,
	title = {Lasso vs. Ridge Regression: A Detailed Comparison},
	url = {https://medium.com/@tyagi.lekhansh/lasso-vs-ridge-regression-a-detailed-comparison-140f7832c624},
	shorttitle = {Lasso vs. Ridge Regression},
	abstract = {In the realm of regression analysis, Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge Regression are two popular…},
	titleaddon = {Medium},
	author = {Lekhansh},
	urldate = {2025-09-22},
	date = {2024-09-04},
	year = {2024},
	langid = {english},
}

@book{hastie_elements_2009,
	location = {New York, {NY}},
	title = {The Elements of Statistical Learning},
	rights = {http://www.springer.com/tdm},
	isbn = {978-0-387-84857-0 978-0-387-84858-7},
	url = {http://link.springer.com/10.1007/978-0-387-84858-7},
	series = {Springer Series in Statistics},
	publisher = {Springer},
	author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
	urldate = {2025-09-22},
	date = {2009},
	year = {2009},
	doi = {10.1007/978-0-387-84858-7},
	keywords = {Averaging, Boosting, classification, clustering, data mining, machine learning, Projection pursuit, Random Forest, supervised learning, Support Vector Machine, unsupervised learning},
	file = {Full Text PDF:/home/lars/Zotero/storage/D3N4DVY9/Hastie et al. - 2009 - The Elements of Statistical Learning.pdf:application/pdf},
}

@book{goodfellow_deep_2016,
	title = {Deep Learning},
	publisher = {{MIT} Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	date = {2016},
	year = {2016},
}

@book{bishop_pattern_2006,
	location = {New York},
	title = {Pattern recognition and machine learning},
	isbn = {978-0-387-31073-2},
	series = {Information science and statistics},
	publisher = {Springer},
	author = {Bishop, Christopher M.},
	date = {2006},
	year = {2006},
	langid = {english},
	file = {PDF:/home/lars/Zotero/storage/9H5W9BGC/Bishop - 2006 - Pattern recognition and machine learning.pdf:application/pdf},
}