1ª aula
Apresentação
1ª aula
Apresentação
2ª aula
Introdução ao Data Mining
3ª aula
Data warehousing e OLAP
4ª aula
Data Cube - Storage space; Partial, total and no materialization (precomputation); How to select cuboids for materialization and how to use them; Indexing; Multiway array aggregation; Constraints for the curse of dimensionality; Discovery driven explanation
5ª aula
Denormalization and ETL
6ª aula
Metodologia de desenvolvimento de DW.
7ª aula
Visualizing one Variable, Statistics for one Variable, Joint Distributions Hypothesis testing, Confidence Intervals
8ª aula
Noise & Data Reduction, Paired Sample t Test, Data Transformation - Overview, From Covariance Matrix to PCA , Fourier Analysis - Spectrum, Dimension Reduction
9ª aula
Machine Learning Overview, Sales Transaction and Association Rules, Aprori Algorithm
10ª aula
Challenges of Frequent Pattern Mining, Improving Apriori, Fp-growth, Fp-tree, Mining frequent patterns with FP-tree, Visualization of Association Rules
11ª aula
What is Cluster Analysis, k-Means, Adaptive Initialization, EM, Learning Mixture Gaussians, E-step, M-step, k-Means vs Mixture of Gaussians
12ª aula
Initial centroids, Validation example, Cluster merit Index, Cluster validation, three approaches, Relative criteria, Validity Index, Dunn Index, Davies-Bouldin (DB) index , Combination of different distances/diameter methods, Tree clustering, Linkage rules, Conceptual Clustering
13ª aula
Conceptual Clustering, COBWEB, Category utility, Sequence clustering, Markov Chains, E-Step and M-Step
14ª aula
(SOM) Self Organizing Maps, I nstance Based Learning, k-Nearest Neighbor Algorithm, (LVQ) Learning Vector Quantization
15ª aula
Uncertainty & Probability, Baye's rule, Choosing Hypotheses- Maximum a posteriori , Maximum Likelihood - Baye's concept learning, Maximum Likelihood of real valued function, Bayes optimal Classifier, Joint distributions, Naive Bayes Classifier
16ª aula
Bayesian networks, Conditional Independence, Inference in Bayesian Networks, Irrelevant variables, Constructing Bayesian Networks, Aprendizagem Redes Bayesianas
17ª aula
Decision tree examples, ID3 algorithm, Occam Razor, Top-Down Induction in Decision Trees, Information Theory, gain
18ª aula
Overfiting, cross-validation, confusion matrix, LIFT, Reduced-Error Pruning, C4.5, From Trees to Rules, Contigency table (statistics)
19ª aula
Inner-product scalar, Perceptron, Perceptron learning rule, XOR problem, linear separable patterns, Gradient descent, Stochastic Approximation to gradient descent, LMS Adaline
20ª aula
Perceptron, Gradient Descent, Multi-layerd neural network, Back-Propagation, More on Back-Propagation, Examples
21ª aula
Back-Propagation, Stochastic Back-Propagation Algorithm, Step by Step Example, Radial Basis-Function Networks, Gaussian response function, Location of center u, Determining sigma, Why does RBF network work
22ª aula
RBF-networks, Support Vector Machines, Good Decision Boundary, Optimization Problem, Soft margin Hyperplane, Non-linear Decision Boundary, Kernel-Trick, Approximation Accurancy
23ª aula
Linear Transformation, Finding the Regression Line, Minimize sum of the quadratic residuals, Curve Fitting, Logistic Regression, Odds and Probability
24ª aula
Overview of SAD
25ª aula
Data Mining as an Application Platform (a), weka Java-based Machine Learning Tool (b)