Basic Information
Introduction to Data Mining Methods and Unsupervised Learning. Review Of Statistical Inference Parametric and Non-parametrics Estimation, Hypothesis Testing. Data Preprocessing. Feature Selection. Dimensionality Reduction# Pca, Svd, Nonlinear Extensions. Distance And Similarity Measures. Clustering Algorithms. Frequency And Association Mining. Outlier Analysis. Representative Applications. Learning Outcomes# Upon Completing The Course, Students Will Be Able To# 1. Explain The Basic Issues of Data Analysis. 2. Explain And Implement Statistical Methods For Parameter Estimation and Hypothesis Testing. 3. Explain and Implement Basic Approaches For Feature Selection. 4. Explain and Implement Algorithms For Data Dimensionality Reduction. 5. Explain and Implement Algorithms For Frequency And Correlation. 6. Explain and Implement Algorithms For Data Clustering. 7. Explain and Implement Algorithms For Outlier Detection.
Faculty: Electrical and Computer Engineering
|Undergraduate Studies
|Graduate Studies
Pre-required courses
(44130 - Signals and Systems and 104034 - Introduction to Probability H) or (44131 - Signals and Systems and 104034 - Introduction to Probability H)