syllabus.tex

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\title{Machine Learning: Syllabus}
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    \section{Supervised Learning}
    \begin{frame}{Supervised Learning}
        Regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbor, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross validation, multi-layer perceptron, feed-forward neural network.
    \end{frame}

    \section{Unsupervised Learning}
    \begin{frame}{Unsupervised Learning}
        Clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple linkage, dimensionality reduction, principal component analysis.
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