This page contains lectures videos
for the data mining course offered at RPI in Fall 2019.

## Aug 30, Introduction, Data Matrix

## Sep 6, Data Matrix: Vector View

## Sep 10, Numeric Attributes: Statistical and Algebraic View

## Sep 13, Covariance Matrix, Eigenvalues, Principal Component Analysis (PCA)

## Sep 17, PCA, Normal Distribution

## Sep 20, High Dimensional Data

## Sep 27, Kernel Method and Kernel PCA

## Oct 4, Linear Regression (Algebraic and Geometric Views)

## Oct 8, Linear Regression: QR Factorization, Ridge Regression

## Oct 11, Linear Regression: Kernel Regression, Logistic Regression

## Oct 15, Logistic Regression

## Oct 22, Neural Networks: Multilayer Perceptrons

## Oct 25, Neural Networks: Deep Networks, Recurrent Networks

## Oct 29, Recurrent Neural Networks (RNNs)

## Nov 5, LSTMs and Convolutional Neural Networks (CNNs)

## Nov 8, Support Vector Machines (SVMs)

## Nov 12, SVMs and Naive Bayes

## Nov 15, Clustering: Kmeans

## Nov 19, Clusering: Expectation Maximization, Spectral Clustering

## Nov 22, Spectral and Graph Clustering, Frequent Pattern Mining

## Nov 26, Frequent Pattern Mining (Itemsets)

## Dec 3, Itemset Mining, Model Evaluation: Bias and Variance

## Dec 6, Model Evaluation: Cross Validation, Emsemble Models