We start with three concrete examples: 1. Data Mining example: Market basket Data analysis 2. Pattern Recognition example: Handwritten letters recognition 3. Cancer prediction using DNA expressions recorded on microarrays From these examples, key ideas, concepts and methods will be introduced. Data mining uses many techniques from Machine Learning and Pattern Recognition. The follow lists the main topics to be covered during the course: Classification -kNN -Naive Bayes -Linear Regression -Support Vector Machine Clustering - K-means clustering - Gaussin Mixture Model and EM Algorithm Data types, preprocessing, normalization, etc Feature Selection - t-statistic, F-statisic - mutual information - mininum reduncy, maximun relevance Dimension Reduction - principle component analysis - linear discriminant analysis Graph Embedding - Embedding a graph (similarity matrix) in a metric space Semi-supervied Learning - Large unclassified data, small number of data have class labels