commit 07b9eca6b3219a81d63b5bb728b57c7b32c77ee4
parent 92efcde0608a9c6e041e2e3adacb4ee52a3b9e48
Author: Andrew Laack <andrew@laack.co>
Date: Sun, 26 Apr 2026 02:31:24 -0500
Added gemini home page
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+# Andrew Laack
+
+## research
+
+My main research interest is classical machine learning. In particular, oblique decision trees, ensemble methods, and interpretability.
+
+## publications
+
+* CART-ELC: Oblique decision tree induction via exhaustive search
+
+=> https://doi.org/10.48550/arxiv.2505.05402
+
+## code
+
+I like building software and implementing machine learning research. My code is self hosted. I have some code available on my Github, but I am not a fan of the platform becoming the de facto standard for code distribution given its proprietary nature.
+
+=> https://git.laack.co/
+=> https://github.com/andrewlaack
+
+## studies
+
+I graduated from the University in Wisconsin-Superior with a BS in CS in the Spring of 2025. I am also starting a Master's degree program at Georgia Tech in the Fall of 2025. I have taken a fair number of computer science courses and math courses, but I have found it much more rewarding to self-study. Below is a non-comprehensive list of books I have read and recommend to those interested in machine learning.
+
+## ml book recommendations (in order)
+
+* Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron
+* The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
+* Deep Learning by Goodfellow, Bengio, and Courville
+* Reinforcement Learning by Sutton and Barto
+
+
+## math book recommendations (unordered)
+
+* Probability and Statistical Inference by Hogg and Tanis
+* Calculus: Early Transcendentals by Stewart
+* Linear Algebra Done Right by Axler
+* Discrete Mathematics and Its Applications by Rosen
+* Mathematics for Machine Learning by Deisenroth
+
+These were the most impactful math and machine learning books that I read early on. For those interested in reading them, I would recommend first reading Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow to get a good understanding of what machine learning is all about and how to use modern machine learning tools. I would then start reading the math books to gain a strong understanding of linear algebra, probability, and calculus, prior to returning to the more theory based machine learning books. Once all of these have been read and well understood, it becomes evident what is of interest to the reader and thus their paths may diverge, but I think these recommendations are non-controversial.
+
+## blog
+
+On my blog I discuss my views about software, freedom, and other topics of interest.
+
+=> https://blog.laack.co/
+
+## contact
+
+Email contained in metadata:
+
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