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Course module: 822047-B-6
822047-B-6
Introduction to Machine Learning
Course info
Course module822047-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Humanities and Digital Sciences; TSH: Department Cognitive Science and AI; TSH: Department Cognitive Science and AI;
Is part of
B Communication and Information Sciences
B Communication and Information Sciences (Spc.Cog.Sc & AI)
B Cognitive Science and Artificial Intelligence
PM Communication- and Information Sciences
Convenant TSH
Minor CIS for Online Culture
Lecturer(s)
Lecturer
dr. E.A. Keuleers
Other course modules lecturer
Lecturer
dr. L.L. Ong
Other course modules lecturer
Lecturer
dr. M. Postma
Other course modules lecturer
Academic year2019
Starting block
SM 2
Course mode
Full-time
Remarks-
Registration openfrom 15/01/2020 up to and including 21/08/2020
Aims
Learning Objectives:

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.


Required Prerequisites

Basic Programming
Statistics for CSAI I
 
Content
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.

Evaluation
 
Pop Quizzes:
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.   
 
Practical Sessions:
Students are required to submit at least 8 out of 12 resulting scripts from the practical session worksheets. These scripts will not be graded.
 
Mid-term Exam:
The mid-term exam which counts for 20 % of the grade
 
Individual Take-home Assignment:
The individual final exam for 40% of the grade
 
Final Exam:
The final exam which counts for 40% of the grade

“Due to limited capacity, this course is currently not open for external students.”​
Course available for exchange students
Conditions of admission apply
Contact person
dr. L.L. Ong
Timetable information
Introduction to Machine Learning
Written test opportunities
DescriptionTestBlockOpportunityDate
Schriftelijk / WrittenEXAM_01SM 2203-07-2020
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Schriftelijk / WrittenEXAM_01SM 2105-06-2020
Required materials
-
Recommended materials
Literature
Muller & Guido, Introduction to Machine Learning with Python. O’Reilly.
Tests
Written

Final Result

Individual Assignment

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Kies de Nederlandse taal