Machine Learning (Master course) |
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| << TO BE UPDATED, PLEASE VISIT AGAIN>> | ||||||||||
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| Lecture Notes: | ||||||||||
| Week | Topics | Notes | Assignments |
Due date/ Remarks |
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| 1 | Motivation and Applications of Machine Learning | download | ||||||||
| 2 | Supervised Learning, Linear Regression, Gradient Descent | download | ||||||||
| Batch Gradient Descent, Stochastic Gradient Descent | ||||||||||
| 3 | The concept of Underfitting and Overfitting, locally waighted regression, Logistic regression, Perceptron | download | ||||||||
| 4 | Newton's Method, General Lineal Models | download | ||||||||
| 5 | Discriminative algorithms. Gaussian Discriminant Analysis | download | ||||||||
| 6 | Nonlinear Classifiers, Neural Networks, Support Vector Machine | download | ||||||||
| 7 | Bias/variance Tradeoff, Uniform Convergence Theorem | download | ||||||||
| 8 | Feature selection, Model selection | download | ||||||||
| 9 | Online learning, Bayesian Statistical and Regularization | download | ||||||||
| 10 | The concept of unsupervised learning, K-means clustering algorithm | download | ||||||||
| 11 | Reestrictions on a Covariance Matrix | download | ||||||||
| 12 | Generalization to Continuous States, Discretization, Curse of Dimensionality | download | ||||||||
| 13 | Dynamical Systems, Linear Quadratic Regulation, Linearizing a Nonlinear Model | download | ||||||||
| 14 | Machine Learning for Predition | download | ||||||||
| 15 | Applications of Machine Learning | download | ||||||||
