index.gmi (3174B)
1 # Andrew Laack 2 3 ## research 4 5 My main research interest is classical machine learning. In particular, oblique decision trees, ensemble methods, and interpretability. 6 7 ## publications 8 9 CART-ELC: Oblique decision tree induction via exhaustive search 10 11 => https://doi.org/10.48550/arxiv.2505.05402 12 13 ## code 14 15 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. 16 17 => https://git.laack.co/ 18 => https://github.com/andrewlaack 19 20 ## studies 21 22 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. 23 24 ## ml book recommendations (in order) 25 26 * Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron 27 * The Elements of Statistical Learning by Hastie, Tibshirani, Friedman 28 * Deep Learning by Goodfellow, Bengio, and Courville 29 * Reinforcement Learning by Sutton and Barto 30 31 32 ## math book recommendations (unordered) 33 34 * Probability and Statistical Inference by Hogg and Tanis 35 * Calculus: Early Transcendentals by Stewart 36 * Linear Algebra Done Right by Axler 37 * Discrete Mathematics and Its Applications by Rosen 38 * Mathematics for Machine Learning by Deisenroth 39 40 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. 41 42 ## blog 43 44 On my blog I discuss my views about software, freedom, and other topics of interest. 45 46 => gemini://blog.laack.co/ 47 48 ## contact 49 50 My email is andrew@laack.co. 51 52 ```PGP Key 53 -----BEGIN PGP PUBLIC KEY BLOCK----- 54 55 mDMEZ6xkphYJKwYBBAHaRw8BAQdAIDyb5c/NfL9VY8GTK2+NWD48GSxk3wBuOA6e 56 kb5O2SS0LWFuZHJldyBsYWFjayAocGFzc3dvcmQga2V5KSA8YW5kcmV3QGxhYWNr 57 LmNvPoiTBBMWCgA7FiEEWEIRDXWw8E+m6MOJMvmjEHDJ1+MFAmesZKYCGwMFCwkI 58 BwICIgIGFQoJCAsCBBYCAwECHgcCF4AACgkQMvmjEHDJ1+MchQEA/qSjA+uE+u/p 59 lwGdAOnmz8qQmw4cettMujU0/WXhCKEBAJxS62RluH2jlYMk0u6XiEW2u9AOse6E 60 6Tt1ya0et/IOuDgEZ6xkphIKKwYBBAGXVQEFAQEHQP4GUQ22EKyTtp5UP+aoPrjb 61 k27LGhvPDtcbw+dyGT4VAwEIB4h4BBgWCgAgFiEEWEIRDXWw8E+m6MOJMvmjEHDJ 62 1+MFAmesZKYCGwwACgkQMvmjEHDJ1+OQhAD/dh4e/qYtE/xjvEJb9foDYxCAZmoO 63 YDCsVpBwLQgBKW8A/RshYmUz7CBCuGsmxPtzVNSsP3mtLTc/85dX1V8oTd0J 64 =wfRT 65 -----END PGP PUBLIC KEY BLOCK----- 66 ```