Faculteit  University College Groningen 
Jaar  2019/20 
Vakcode  UCGSC222 
Vaknaam  Machine Learning 
Niveau(s)  bachelor 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Machine Learning  
Leerdoelen  1. To understand the term “Machine Learning (ML)” and its applications. 2. To learn the available ML libraries and their applications. 3. To learn how to manipulate data using ML methods. 4. To understand the abstraction of the ML methods, to implement them and to compare the implementation to the existing Python libraries. 5. To understand the differences between supervised learning and unsupervised learning. 6. To be able to decide on the appropriate ML methods for a given problem. 7. To be able to build ML models and to understand their strengths and limitations 

Omschrijving  The vast amount of data generated every day induced the development of computer programs that use statistical models and algorithms to make decisions based on the available data. This branch of statistical sciences that incorporates knowledge from statistics, computer science, numerical analysis and mathematics is called “Machine Learning”. In this course students will learn supervised learning methods such as linear regression, least square fitting, logistic regression, decision trees, support vector machine and will be introduced to neural networks. In addition, for unsupervised learning the course will cover kmean clustering, hierarchical clustering and principle component analysis. Python will be the main programming language for the course. Scikitlean, numpy and tensorflow libraries will be used.  
Uren per week  8  
Onderwijsvorm  Computer Lab, Lecture  
Toetsvorm  Practical, Written exam  
Vaksoort  bachelor  
Coördinator  M.A.A. Amin, PhD.  
Docent(en)  M.A.A. Amin, PhD.  
Entreevoorwaarden  Knowledge of Python programming is very helpful but not required.  
Opmerkingen  The course is useful for students planning to pursue a career in machine learning, data science, data management, digital marketing or economy.  
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