Subject description - BE5B33RPZ
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Explanatory Notes
Instructions
| BE5B33RPZ | Pattern Recognition and Machine Learning | ||
|---|---|---|---|
| Roles: | PV, P | Extent of teaching: | 2P+2C |
| Department: | 13133 | Language of teaching: | EN |
| Guarantors: | Matas J. | Completion: | Z,ZK |
| Lecturers: | Drbohlav O., Matas J. | Credits: | 6 |
| Tutors: | Drbohlav O., Matas J., Neumann L., Šochman J. | Semester: | Z |
Web page:
https://cw.fel.cvut.cz/wiki/courses/BE5B33RPZAnotation:
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.Study targets:
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).Course outlines:
| 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. |
Exercises outline:
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 |
Literature:
| 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. |
Requirements:
Knowledge of linear algebra, mathematical analysis and probability and statistics.Note:
| https://cw.felk.cvut.cz/doku.php/courses/ae4b33rpz/lectures/start |
Keywords:
pattern recognition, statistical decision-making, machine learning, classification Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| BPOI_BO_2018 | Common courses | PV | – |
| BPOI4_2018 | Computer Games and Graphics | PV | – |
| BPOI3_2018 | Software | PV | – |
| BPOI2_2018 | Internet of Things | PV | – |
| BPOI1_2018 | Artificial Intelligence and Computer Science | PV | – |
| BEECS | Common courses | PV | 5 |
| BPOI1_2016 | Computer and Information Science | P | – |
| BPOI_BO_2016 | Common courses | P | – |
| BPOI4_2016 | Computer Games and Graphics | P | – |
| BPOI3_2016 | Software | P | – |
| BPOI2_2016 | Internet of Things | P | – |
| BPEECS_2018 | Common courses | PV | 5 |
| Page updated 14.12.2024 17:50:57, semester: L/2024-5, Z/2025-6, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |