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Návod
Webová stránka:
https://cw.fel.cvut.cz/wiki/courses/BE5B33RPZ
Anotace:
The basic formulations of the statistical decision problem are presented. The necessary knowledge about the (statistical) relationship between observations and classes of objects is acquired by learning on the raining set. The course covers both well-established and advanced classifier learning methods, as Perceptron, AdaBoost, Support Vector Machines, and Neural Nets.
This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at
https://prg.ai/minor.
Cíle studia:
To teach the student to formalize statistical decision
making problems, to use machine learning techniques and to
solve pattern recognition problems with the most popular
classifiers (SVM, AdaBoost, neural net, nearest neighbour).
Osnovy přednášek:
| 1. | | Introduction. Basic notions. The Bayesian recognition problem |
| 2. | | Non-Bayesian tasks |
| 3. | | Parameter estimation of probabilistic models. Maximum likelihood method |
| 4. | | Nearest neighbour method. Non-parametric density estimation. |
| 5. | | Logistic regression |
| 6. | | Classifier training. Linear classifier. Perceptron. |
| 7. | | SVM classifier |
| 8. | | Adaboost learning |
| 9. | | Neural networks. Backpropagation |
| 10. | | Cluster analysis, k-means method |
| 11. | | EM (Expectation Maximization) algorithm. |
| 12. | | Feature selection and extraction. PCA, LDA. |
| 13. | | Decision trees. |
Osnovy cvičení:
You will implement a variety of learning and inference algorithms on simple pattern recognition tasks. Each week a new assignment is introduced at the beginning of the lab, and you are expected to complete the task during the submission period. The discussion at the beginning of the lab session will link the theory presented in the lectures to the practical task in the weekly assignments. The remaining time of the lab is devoted to individual interactions between students and teaching assistants.
| 1. | | Introduction, work with python, simple example |
| 2. | | Bayesian decision task |
| 3. | | Non-bayesian tasks - the minimax task |
| 4. | | Non-parametrical estimates - parzen windows |
| 5. | | MLE, MAP and Bayes parameter estimation |
| 6. | | Logistic regression |
| 7. | | Problem solving / exam questions |
| 8. | | Linear classifier - perceptron |
| 9. | | Support Vector Machine |
| 10. | | AdaBoost |
| 11. | | K-means clustering |
| 12. | | Convolutional neural networks |
| 13. | | Problem solving / exam questions |
Literatura:
| 1. | | Duda, Hart, Stork: Pattern Classification, 2001. |
| 2. | | Bishop: Pattern Recognition and Machine Learning, 2006. |
| 3. | | Schlesinger, Hlavac: Ten Lectures on Statistical and Structural Pattern Recognition, 2002. |
Požadavky:
Knowledge of linear algebra, mathematical analysis and
probability and statistics.
Poznámka:
Klíčová slova:
pattern recognition, statistical decision-making, machine learning, classification
Předmět je zahrnut do těchto studijních plánů:
| Stránka vytvořena 6.12.2024 17:50:34, semestry: Z/2025-6, Z,L/2024-5, připomínky k informační náplni zasílejte správci studijních plánů |
Návrh a realizace: I. Halaška (K336), J. Novák (K336) |