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