MLWelcome Updated 
ML (Machine Learning
4. Semester: Datamatician Course (Computer Science)

Welcome to ML Links to literature requirements

ML is short for "Machine Learning"

Be aware that ML requires group discussion as well as reading by the individual student.

The major topics at 4. semester are:

Practical exercises:

Great emphasize is given to your understanding of the basic principles and to your technical skills, too. Therefore you have to solve some discussion assignments and many practical exercises. Some can be solved at the school using your teachers and class mates carefull guidance/help. Others you have to work with, and to solve at home or in your study group.

Teaching Principles
The teaching will be based on  classic lectures, discussions, smaller practical assignments, groupwork and individual work and team building/work on the on the mandatory assignments.
Class (10 weeks): Lectures 25%, Hands on Individual Assignments 30%, Group work 35%, Mandatory Projects 25%
Individual Synopsis (5 weeks research)

Relations with other subjects
ML is related to:

Mandatory assignments
There are 2 minor mandatory assignmets in ML solved on the fly.
This will involve groupwork of 4-5 students as well as individual solution.
The mandatory assignments must be passed.

There is one exam held after 4. semester.

  • An oral individual exam in the individual synopsis (5 weeks research)
  • To proceed to the 5th Semester, all mandatory assignments must be passed and a minimum of 02 must be achieved at the exam.

    Program and developing enviroment
    There are various developing enviroments for building and testing models in ML.
    We shall use Anaconda for several reasons:

    Maintenance by