People Innovation Excellence


Learning Outcomes:

On successful completion of this course, student will be able to: Describe what machine learning is about; Explain probability and stochastic processes of discrete variables and continous variables; Modify parameter of modeling function; Experiment of cost function in linear estimation; Construct learning algorithm.


  1. Introduction to machine learning (classification and regression);
  2. Probability and stochastic processes I (Probability, discrete random variables, distribution example of discrete variables);
  3. Probability and stochastic processes II (continuous random variables, mean and variance, transformation of random variables, distribution example of continuous variables);
  4. Learning in Parametric Modeling I (Parameter estimation, linear regression, classification);
  5.  Learning in Parametric Modeling II (Regularization, maximum likelihood method, Bayesian inference);
  6. Mean-Square error linear estimation I (The cost function surface, a geometric viewpoint (Orthogonality condition);
  7. Mean-Square error linear estimation II (Extension to complex-valued variables and linear filtering);
  8. Bayesian classification;
  9. Decision (Hyper)surfaces (SVM);
  10. The Naïve Bayes Classifier;
  11. The Nearest Neighbor Rule;
  12. Logistic Regression;
  13. Classification Trees.

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