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<!DOCTYPE html>
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<title>Practical Statistical Learning (PSL) — PSL_Book 0.1 documentation</title>
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<li class="toctree-l1"><a class="reference internal" href="w1/w1_index.html">1. Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="w2/w2_index.html">2. Linear Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="w3/w3_index.html">3. Variable Selection and Regularization</a></li>
<li class="toctree-l1"><a class="reference internal" href="w4/w4_index.html">4. Regression Trees and Ensemble</a></li>
<li class="toctree-l1"><a class="reference internal" href="w5/w5_index.html">5. Nonlinear Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="w6/w6_index.html">6. Clustering Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="w7/w7_index.html">7. Latent Structure Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="w8/w8_index.html">8. TBA</a></li>
<li class="toctree-l1"><a class="reference internal" href="w9/w9_index.html">9. Discriminant Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="w10/w10_index.html">10. Logistic Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="w11/w11_index.html">11. Support Vector Machine</a></li>
<li class="toctree-l1"><a class="reference internal" href="w12/w12_index.html">12. Classification Trees and Boosting</a></li>
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<section id="practical-statistical-learning-psl">
<h1>Practical Statistical Learning (PSL)<a class="headerlink" href="#practical-statistical-learning-psl" title="Link to this heading"></a></h1>
<p>(Often humorously dubbed “Pumpkin Spice Latte” by our online students.)</p>
<p>These materials have been curated from a course in statistical learning, developed by Professors <strong>John Marden</strong> (jimarden AT illinois DOT edu) and <strong>Feng Liang</strong> (liangf AT illinois DOT edu) at the University of Illinois Urbana-Champaign (UIUC). The course predominantly draws upon the seminal text <a class="reference external" href="https://hastie.su.domains/ElemStatLearn/">The Elements of Statistical Learning</a> by Hastie, Tibshirani, and Friedman.</p>
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<p>This project is under active development.</p>
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<li class="toctree-l1"><a class="reference internal" href="w1/w1_index.html">1. Introduction</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w1/w1_1_index.html">1.1. Introduction to statistical learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="w1/w1_2_index.html">1.2. Least squares vs. nearest neighbors</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="w2/w2_index.html">2. Linear Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w2/w2_1.html">2.1. Multiple linear regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="w2/w2_2.html">2.2. Geometric interpretation</a></li>
<li class="toctree-l2"><a class="reference internal" href="w2/w2_3.html">2.3. Practical issues</a></li>
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</li>
<li class="toctree-l1"><a class="reference internal" href="w3/w3_index.html">3. Variable Selection and Regularization</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w3/w3_1_subset.html">3.1. Subset Selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="w3/w3_2_regularize.html">3.2. Regularization</a></li>
<li class="toctree-l2"><a class="reference internal" href="w3/w3_3_ridge.html">3.3. Ridge Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="w3/w3_4_lasso.html">3.4. Lasso Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="w3/w3_5_discussion.html">3.5. Discussion</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w4/w4_index.html">4. Regression Trees and Ensemble</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w4/w4_1_reg_tree.html">4.1. Regression Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="w4/w4_2_randomforest.html">4.2. Random Forest</a></li>
<li class="toctree-l2"><a class="reference internal" href="w4/w4_3_gbm.html">4.3. GBM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w5/w5_index.html">5. Nonlinear Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w5/w5_1_poly.html">5.1. Polynomial Regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="w5/w5_2_spline.html">5.2. Cubic Splines</a></li>
<li class="toctree-l2"><a class="reference internal" href="w5/w5_3_reg_spline.html">5.3. Regression Splines</a></li>
<li class="toctree-l2"><a class="reference internal" href="w5/w5_4_smoothing_spline.html">5.4. Smoothing Splines</a></li>
<li class="toctree-l2"><a class="reference internal" href="w5/w5_5_local_reg.html">5.5. Local Regression</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w6/w6_index.html">6. Clustering Analysis</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w6/w6_1_distance.html">6.1. Distance Measures</a></li>
<li class="toctree-l2"><a class="reference internal" href="w6/w6_2_kmeans.html">6.2. K-means and K-medoids</a></li>
<li class="toctree-l2"><a class="reference internal" href="w6/w6_3_choice_of_k.html">6.3. Choice of K</a></li>
<li class="toctree-l2"><a class="reference internal" href="w6/w6_4_hcluster.html">6.4. Hierarchical Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="w6/w6_5_code.html">6.5. R/Python Code</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w7/w7_index.html">7. Latent Structure Models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w7/w7_1_intro.html">7.1. Model-based Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="w7/w7_2_mixture.html">7.2. Mixture Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="w7/w7_3_EM.html">7.3. The EM Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="w7/w7_4_LDA.html">7.4. Latent Dirichlet Allocation Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="w7/w7_5_hmm.html">7.5. Hidden Markov Models</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w8/w8_index.html">8. TBA</a></li>
<li class="toctree-l1"><a class="reference internal" href="w9/w9_index.html">9. Discriminant Analysis</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_1_intro_classification.html">9.1. Introduction to Classification</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_2_DA.html">9.2. Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_3_QDA.html">9.3. Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_4_LDA.html">9.4. Linear Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_5_FDA.html">9.5. Fisher Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_6_NB.html">9.6. Naive Bayes Classifiers</a></li>
<li class="toctree-l2"><a class="reference internal" href="w9/w9_7_summ.html">9.7. Summary</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w10/w10_index.html">10. Logistic Regression</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w10/w10_1_setup.html">10.1. Setup</a></li>
<li class="toctree-l2"><a class="reference internal" href="w10/w10_2_mle.html">10.2. MLE</a></li>
<li class="toctree-l2"><a class="reference internal" href="w10/w10_3_seperable.html">10.3. Seperable Data</a></li>
<li class="toctree-l2"><a class="reference internal" href="w10/w10_4_code.html">10.4. R/Python Code</a></li>
<li class="toctree-l2"><a class="reference internal" href="w10/w10_5_sampling.html">10.5. Retrospective Sampling Data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w11/w11_index.html">11. Support Vector Machine</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w11/w11_0_intro.html">11.1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="w11/w11_1_separable_case.html">11.2. The Separable case</a></li>
<li class="toctree-l2"><a class="reference internal" href="w11/w11_2_non_separable_case.html">11.3. The Non-separable case</a></li>
<li class="toctree-l2"><a class="reference internal" href="w11/w11_4_nonlinear.html">11.4. Nonlinear SVMs</a></li>
<li class="toctree-l2"><a class="reference internal" href="w11/w11_6_appendix.html">11.5. Appendix</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w12/w12_index.html">12. Classification Trees and Boosting</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w12/w12_1_intro.html">12.1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="w12/w12_2_measures.html">12.2. Impurity Measures</a></li>
<li class="toctree-l2"><a class="reference internal" href="w12/w12_3_compare.html">12.3. Misclassification Rate vs. Entropy</a></li>
<li class="toctree-l2"><a class="reference internal" href="w12/w12_4_aAaboost.html">12.4. AdaBoosting</a></li>
<li class="toctree-l2"><a class="reference internal" href="w12/w12_5_boost.html">12.5. Forward Stagewise Additive Modeling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="w13/w13_index.html">13. Recommender System</a><ul>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_1_intro.html">13.1. Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_2_Content_based.html">13.2. Content-Based Methods</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_3_CF.html">13.3. Collaborative Filtering</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_4_CF_details.html">13.4. UBCF and IBCF</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_5_LatentFactor.html">13.5. Latent Factor Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_6_practice.html">13.6. Challenges and Strategies</a></li>
<li class="toctree-l2"><a class="reference internal" href="w13/w13_7_deep_RS.html">13.7. Deep Recommender Systems</a></li>
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