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      1 <!DOCTYPE html>
      2 <html lang="en"><head>
      3 <meta http-equiv="content-type" content="text/html; charset=UTF-8">
      4   <meta charset="UTF-8">
      5   <title>Andrew Laack</title>
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    105 
    106 <meta name="description" content="Andrew Laack's personal website showcasing research, machine learning projects, work experience, and studies in computer science. Explore code repositories, book recommendations, and find my contact information." />
    107 </head>
    108 <body>
    109 
    110     <p id="msg"></p>
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    116 
    117 
    118     <div id="main">
    119 
    120 
    121 
    122 	<h1>
    123 		Andrew Laack
    124 	</h1>
    125 
    126 	<nav class="navigation">
    127 	  <a href="#research">research</a>
    128 	  <span class="separator">|</span>
    129 	  <a href="#code">code</a>
    130 	  <span class="separator">|</span>
    131 	  <a href="#studies">studies</a>
    132 	  <span class="separator">|</span>
    133 	  <a href="#blog">blog</a>
    134 	  <span class="separator">|</span>
    135 	  <a href="#contact">contact</a>
    136 	</nav>
    137 
    138 
    139   <hr>
    140   <h2 id="research">research</h2>
    141   <p> My main research interest is classical machine learning. In particular, oblique decision trees, ensemble methods, and interpretability.
    142 	
    143 <h3 id="publications">publications</h3>
    144 <ul>
    145   <li>
    146 <strong>Laack, A. D.</strong> (2025, May 8). <em>CART-ELC: Oblique decision tree induction via exhaustive search</em> [Manuscript under review]. arXiv. <a href="https://doi.org/10.48550/arxiv.2505.05402" target="">https://doi.org/10.48550/arxiv.2505.05402</a>
    147   </li>
    148 </ul>
    149 
    150 
    151   <hr>
    152   <h2 id="code">code</h2>
    153         <p>I like building software and implementing machine learning research. <a href="https://git.laack.co"> My code</a> is self hosted. I have some code available on my <a href="https://github.com/andrewlaack">Github</a>, but I am not a fan of the platform becoming the de facto standard for code distribution given its proprietary nature.
    154 
    155   <hr>
    156   <h2 id="studies">studies</h2>
    157 
    158   <p>
    159   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.
    160   </p>
    161 
    162   <p>
    163     <h3>ml book recommendations (in order)</h3>
    164     <ol>
    165 		<li> Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron</li>
    166 		<li> The Elements of Statistical Learning by Hastie, Tibshirani, Friedman </li>
    167         <li> Deep Learning by Goodfellow, Bengio, and Courville</li>
    168 		<li> Reinforcement Learning by Sutton and Barto</li>
    169     </ol>
    170 
    171   </p>
    172   <p>
    173 	<h3> math book recommendations (unordered)</h3>
    174     <ol>
    175 		<li> Probability and Statistical Inference by Hogg and Tanis</li>
    176 		<li> Calculus: Early Transcendentals by Stewart </li>
    177 		<li> Linear Algebra Done Right by Axler </li>
    178 		<li> Discrete Mathematics and Its Applications by Rosen</li>
    179 		<li> Mathematics for Machine Learning by Deisenroth</li>
    180     </ol>
    181   </p>
    182 
    183 	<p>
    184 		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.
    185 	</p>
    186 
    187   <hr>
    188   <h2 id="blog">blog</h2>
    189         <p>
    190 On <a href="https://blog.laack.co" target="">my blog</a> I discuss my views about software, freedom, and other topics of interest.
    191         </p>
    192 
    193   <hr>
    194   <h2 id="contact">contact</h2>
    195 
    196   <p> Email contained in metadata: </p>
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