====== Homeworks ======

This page contains all the homework assignments for the course. During the seminars, we will assign and explain practical tasks. You are required to implement solutions in Python and submit your code for automatic evaluation. In some cases, you may also be asked to write a PDF report which will be manually graded.


<note important> You need to obtain at least 50% of the total of regular points from the homeworks. </note> 
===== General Instructions =====
  * All code must be written in **Python**.
  * Each assignment will have a specified **deadline** and **point value**.
  * Code submissions will be evaluated automatically using the **BRUTE** system.
  * If a report is required, it should be submitted along with your code in a **.pdf** format.
  * Follow the specific submission guidelines provided in each assignment.
  * In case of any issues with the automatic evaluation, reach out to **Jakub Paplhám** at <paplhjak@fel.cvut.cz>.
    * Or you can come to ask him in person at the 2nd and 3rd seminars every Thursday.
  
===== List of Assignments =====

  * [[hw_confidence_interval|Homework 1: Prediction Interval]] - Deadline: 6.11.2025
  * [[hw_generalization_bound|Homework 2: Generalization Bound]] - Deadline: 13.11.2025

/*
  * [[hw_histogram_classifier|Homework 3: Histogram Classifier]] - Deadline: 20.11.2024
  * [[hw_neural_network|Homework 4a: Neural Network]] - Deadline: 11.12.2024
  * [[hw_neural_network_report|Homework 4b: Neural Network Report]] - Deadline: 11.12.2024
  * [[hw_ensembles|Homework 5: Ensembles]] - Deadline: 31.12.2024
  * [[hw_maximum_likelihood|Homework 6: Maximum Likelihood Estimator]] - Deadline: 1.1.2025
  * [[hw_em_prior_shift|Homework 7: Expectation-Maximization Algorithm]] - Deadline: 8.1.2025
  * 
*/

 
===== Environment ===== 
The tasks are evaluated automatically using the BRUTE Automatic Evaluation.

The evaluation is performed using Python 3.11 in Docker: [[ https://hub.docker.com/r/brute/brute_python|BRUTE Python Docker ]].