Data Mining and Analysis Kurzusok

Data preprocessing

1. Data Cleaning
2. Data integration and transformation
3. Data reduction
4. Discretization and concept hierarchy generation

Statistical inference

1. Probability distributions, Random variables, Central limit theorem
2. Sampling
3. Confidence intervals
4. Statistical Inference
5. Hypothesis testing

Multivariate linear regression

1. Specification
2. Subset selection
3. Estimation
4. Validation
5. Prediction

Classification methods

1. Logistic regression
2. Linear discriminant analysis
3. K-nearest neighbours
4. Naive Bayes
5. Comparison of Classification methods

Neural Networks

1. Fitting neural networks
2. Training neural networks issues

Decision trees

1. Regression trees
2. Classification trees
3. Trees Versus Linear Models

Bagging, Random Forests, Boosting

1. Bagging
2. Random Forests
3. Boosting

Support Vector Machines and Flexible disct

1. Maximal Margin classifier
2. Support vector classifiers
3. Support vector machines
4. 2 and more classes SVM’s
5. Relationship to logistic regression

Principal Components Analysis

Clustering

1. K-means clustering
2. K-medoids clustering
3. Hierarchical clustering
4. Density based clustering

Model Assesment and Selection

1. Bias, Variance and Model complexity
2. In-sample prediction error
3. The Bayesian approach
4. Cross-validation
5. Bootstrap methods