Topic introductions for Machine Learning
- Assessing and Comparing Classification Algorithms
- Cross validation
- ROC analysis
- Classification
- Decision Trees
- K-nearest neighbor
- Kernel methods
- SVM
- Clustering
- Biclustering
- ISODATA
- k-means
- Kohonen networks
- Mean shift
- Spectral Clustering/Graph cuts
- Data Mining
- Association rules
- Data streams
- Multi-media
- Sensor data
- Text
- Dimensionality reduction
- LDA/Linear Discrimination Analysis
- Multidimensional Scaling
- PCA/Principal Component Analysis/Eigenfeatures
- Ensemble learning methods
- Arcing
- Bagging
- Boosting
- Evolutionary Computation
- Evolutionary Algorithms
- Genetic Algorithms
- Generative methods
- Bayesian Network Inference
- Belief propagation
- Junction tree
- Loopy belief propagation
- MCMC
- Gibbs sampling
- Expectation propagation
- Hidden Markov models
- Branching
- Ergodic
- Linear
- Markov random fields
- Learning theory
- Bounds (Chernoff etc.)
- No free lunch theorem
- PAC/Probably Approximately Correct learning
- VC/Vapnik-Chervonenkis dimensions
- Model selection
- MDL
- Occam's Razor
- BIC
- AIC
- Neural networks
- Feed-forward Networks
- Hopfield Networks
- Learning Vector Quantization
- Mixture of Experts, Hierarchical Mixture of Experts
- Recurrent Networks
- Radial Basis Function networks
- Parameter estimation/Optimization techniques
- Maximum likelihood
- Maximum aposteriori estimate
- Expectation maximization
- Gradient descent
- Regression
- Generalized linear regression
- Local polynomial smoothing/ spline fitting
- Gaussian process regression
- Reinforcement Learning / Q-learning
- Significant applications
- Bioinformatics
- Biometrics
- Credit card data
- Supermarket purchase data
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© 2008 Robert Fisher