At the end of the course, students will be able to:
- implement solutions to real-world machine learning problems;
- describe fundamental concepts in machine learning;
- describe most widely used machine learning algorithms, their advantages and shortcomings;
- use Python libraries for the purposes of model building, evaluation, and parameter learning.
Statistics for CSAI I
This course provides an introduction to machine learning – extracting knowledge from data - using Python and accompanying libraries. Machine learning is applied in all domains of every day life, from music and film recommendations to financial decisions, security, personalized health care, and practical research.|
Attendance of the plenary meetings is mandatory for pop-quizzes. In order to gain access to the final exam, students must take at least 8 of the 12 pop-quizzes.
Students are required to submit at least 8 out of 12 resulting scripts from the practical session worksheets. These scripts will not be graded.
The mid-term exam which counts for 20 % of the grade
Individual Take-home Assignment:
The individual final exam for 40% of the grade
The final exam which counts for 40% of the grade
“Due to limited capacity, this course is currently not open for external students.”