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