Table of Contents

Labs

Winter semester 2020/2021

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.

Due to the closure of the university for full-time teaching, we will move the labs to the Zoom.us application. We will send you the respective lab link via your university email in advance of the actual lab.

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
22.9. / 24.9. introduction, work with python, simple example
29.9. / 1.10. bayesian decision task
6.10. / 8.10. non-bayesian tasks - the minimax task
13.10. / 15.10. MLE, MAP and Bayes parameter estimation *
20.10. / 22.10. non-parametrical estimates - parzen windows
27.10. / 29.10. logistic regression
3.11. / 5.11. exam questions * practice tasks
10.11. / 12.11. linear classifier - perceptron
24.11. / 19.11. support vector machines
1.12. / 26.11. support vector machines 2
8.12. / 3.12. adaboost *
5.1. / 10.12. k-means clustering
15.12. / 17.12. convolutional neural networks *
12.1. / 7.1. zápočet, exam questions

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