Removed homeworks

Author: VaclavMacha

docs/make.jl

@@ -81,32 +81,24 @@ lecture_07 = [
     "Package development" => "./lecture_07/develop.md",
 ]
 
-finalproject = joinpath.("./final_project/", [
-    "homeworks.md",
-    "project.md",
-])
-
 lecture_08 = joinpath.("./lecture_08/", [
     "theory.md",
     "unconstrained.md",
     "constrained.md",
     "exercises.md",
-    "homework.md",
 ])
 
 lecture_09 = joinpath.("./lecture_09/", [
     "theory.md",
     "linear.md",
     "logistic.md",
     "exercises.md",
-    "homework.md",
 ])
 
 lecture_10 = joinpath.("./lecture_10/", [
     "theory.md",
     "nn.md",
     "exercises.md",
-    "homework.md",
 ])
 
 lecture_11 = joinpath.("./lecture_11/", [
@@ -149,7 +141,6 @@ makedocs(;
         "5: Packages" => lecture_05,
         "6: Type system and generic programming" => lecture_06,
         "7: Code organization" => lecture_07,
-        "Course requirements" => finalproject,
         "8: Optimization" => lecture_08,
         "9: Regression and classification" => lecture_09,
         "10: Neural networks I." => lecture_10,

docs/src/final_project/homeworks.md

@@ -13,22 +13,8 @@ gr()
 Welcome to our course *Julia for Optimization and Learning*. This course consists of two parts:
 - *Basics of Julia*: [Julia](https://julialang.org/) is a fast programming language for scientific computing. Designed and developed at MIT, it quickly keeps gaining popularity and scored rank 22 among programming languages in the [PYPL rating](https://pypl.github.io/PYPL.html) (as of September 2021).
 - *Applications*: The second part of the course will be dedicated to applications. The main emphasis will given to machine learning, but we will also go through statistics and differential equations.
-Since there are no course requirements, all students who want to learn to program efficiently or machine learning techniques are welcome to join.
-
-The course's main requirement is to finish a small project of your choice. This project can be connected to your bachelor or master thesis, and we hope that our course will help you prepare a better thesis.
-
-
-## Information about the course
-
-- Teachers: Lukáš Adam, Václav Mácha
-- Credits: 4
-- FEL code: [B0B36JUL](http://bilakniha.cvut.cz/en/predmet6985806.html)
-- Requirements for the course:
-    - Finish three [homework](@ref homeworks). Each homework is a short task.
-    - Finish one final [project](@ref project). Project is a more complex task, where you can choose the topic.
-- Grading: The attendance and homework are compulsory to pass the course. The final grade will be based solely on the project.
-- Teaching way: online via MS Teams. CTU students will be added automatically to a team after registering for the course in [KOS](https://www.kos.cvut.cz). Students from other universities should write us an email.
 
+Since there are no course requirements, all students who want to learn to program efficiently or machine learning techniques are welcome to join.
 
 ## What will we emphasize?