Condensed-MachineLearning/CS760Notes.aux at master · ChristianBorst/Condensed-MachineLearning · GitHub

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\relax
\@writefile{toc}{\contentsline {section}{\numberline {1}Decision Tree Learning}{3}}
\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Information Gain}{3}}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Instance-Based Learning}{4}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}K-nearest Neighbor}{4}}
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\@writefile{toc}{\contentsline {section}{\numberline {3}Probability and Bayesian Learning}{5}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Probabilistic Machine Learning Concepts}{5}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Expectation Maximization}{6}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Learning Network Structure}{6}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.5}Naive Bayes and Tree Augmented Network}{8}}
\@writefile{toc}{\contentsline {section}{\numberline {4}Machine Learning Methdology}{9}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Partitioning of Data}{9}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Performance Evaluation}{9}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Confidence Intervals and Learning Tasks}{10}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Comparing Learning Models and Hypotheses}{10}}
\@writefile{toc}{\contentsline {section}{\numberline {5}Computational Learning Theory}{12}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}PAC Learning}{12}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Hypothesis Spaces}{13}}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Mistake Bound}{13}}
\@writefile{toc}{\contentsline {section}{\numberline {6}Ensemble Methods}{15}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Ensembles and Bagging}{15}}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Adaptive Boosting}{15}}
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\@writefile{toc}{\contentsline {section}{\numberline {7}Neural Networks and Deep Learning}{17}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.1}Perceptrons}{17}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.2}Neural Network Gradient Descent}{17}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.3}Backpropagation and Network Representation}{18}}
\@writefile{toc}{\contentsline {subsection}{\numberline {7.4}Deep Learning}{19}}
\@writefile{toc}{\contentsline {section}{\numberline {8}Support Vector Machines}{21}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.1}Linear Classifiers}{21}}
\@writefile{toc}{\contentsline {subsection}{\numberline {8.2}Nonlinear Classifiers}{21}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {8.4}SVMs by Sequential Minimal Optimization}{23}}
\@writefile{toc}{\contentsline {section}{\numberline {9}Reinforcement Learning}{25}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.1}Learning Value Function}{25}}
\@writefile{toc}{\contentsline {subsection}{\numberline {9.2}Q Function Learning}{25}}
\@writefile{toc}{\contentsline {section}{\numberline {10}Rule Learning and Inductive Logic Programming}{27}}
\@writefile{toc}{\contentsline {subsection}{\numberline {10.1}Relational Learning via FOIL}{27}}
\@writefile{toc}{\contentsline {subsection}{\numberline {10.2}Background on Rule Learning and ILP}{28}}