Table of Contents

Labs

Winter semester 2021/2022

Basic info

Where and when: computer lab KN:E-132 at Building E on Charles square, Tuesday 11:00-12:30 and Thursday 12:45-14:15

If you are new to CTU, see the checklist for visiting students.

What can you expect: The labs require you to implement learning and inference algorithms for a variety of classifiers. Your implementations will be tested with different 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. See the detailed rules below.

What do we expect: Basic knowledge of Python (check the links in the first lab's text if you need a help with this).

Important Links:

Teachers:

Student forum for assistance with assignments

There is a discussion forum administered for this course that can be used to solicit help for the assignments. It is monitored by the lab assistants and it is the preferred form of communication for giving assistance for the assignments since all students can see the question and answer threads. Please check the forum first if you have some confusion about an assignment.

Assignment plan

Date (Tue/Thu) Topic Test
21.9. / 23.9. introduction, work with python, simple example
5.10. / 30.9. bayesian decision task
12.10. / 7.10. non-bayesian tasks - the minimax task
19.10. / 14.10. MLE, MAP and Bayes parameter estimation *
26.10. / 21.10. non-parametrical estimates - parzen windows
2.11. / 4.11. logistic regression
9.11. / 11.11. exam questions * practice tasks
16.11. / 18.11. linear classifier - perceptron
23.11. / 25.11. support vector machines
30.11. / 2.12. support vector machines 2
7.12. / 9.12. adaboost *
14.12. / 16.12. convolutional neural networks
4.1. / 6.1. k-means clustering *

There will be a short test at the beginning of the labs denoted with *. The questions in the tests will refer to material presented in prior lectures.

Exercises

In order to perform well in the lab tests and the exam it is important to follow the examples solved in the class and prepare by solving typical problems. This year we are creating an exercise book containing problems related to the lectures and labs and containing test examples from previous years with solutions.

rpz_exercise_book.pdf

We will keep updating it during the semester, so keep checking for a newer version from time to time.

Please, report any issues or corrections to shekhovtsov@gmail.com.

Requirements to obtain the credit ("zápočet")

Solution submission and evaluation

Lab evaluation

Abscence