IAPR

Topic introductions for Machine Learning



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

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© 2008 Robert Fisher