Time to look for more advanced methods in Machine Learning

  1. Kernel Methods, beyond the SVM
    • The idea of the Kernel
    • The inner product as a kernel
    • Positive Defined Kernel
    • Kernel-induced vector spaces
    • Algorithms:
      • Kernel PCA
      • Feature Selection
      • Kernel methods for cluster analysis
      • Kernel-based regression
      • Kernel ridge regression for supervised classification
      • Support vector learning models for outlier detection
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  2. Adaptive Kernels from Data

  3. Bayesian Learning in Approximate Inference

  4. Bayesian Learning in Non-parametric Models

  5. Factorial Hidden Markov Models

  6. Time-Varying Dynamic Bayesian Networks

  7. Variational Methods in Classification

  8. Gaussian Process Inference using Variational Methods

  9. Compress Sensing

  10. Dimensionality Reduction

  11. A Tour in Feature Engineering

  12. Markov Random Fields and its aplications

  13. Beyond the Stochastic Gradient Descent

  14. Auto Supervised Learning