Code: BE5B33RPZ |
Pattern Recognition and Machine Learning |
Lecturer: prof. Ing. Jiøí Matas Ph.D. |
Weekly load: 2P+2C |
Completion: A, EX |
Department: 13133 |
Credits: 6 |
Semester: W |
- Description:
-
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.
- Contents:
-
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.
- Seminar contents:
-
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
- Recommended literature:
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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.
- Keywords:
- pattern recognition, statistical decision-making, machine
learning, classification
Abbreviations used:
Semester:
- W ... winter semester (usually October - February)
- S ... spring semester (usually March - June)
- W,S ... both semesters
Mode of completion of the course:
- A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
- GA ... Graded Assessment (a grade is awarded for this course)
- EX ... Examination (a grade is awarded for this course)
- A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)
Weekly load (hours per week):
- P ... lecture
- C ... seminar
- L ... laboratory
- R ... proseminar
- S ... seminar