Leren

Learning

Studiewijzer 2017/2018

Globale inhoud

Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. One case where learning is necessary is when human expertise does not exist, or when humans are unable to explain their expertise. Already, there are many successful applications of machine learning in various domains: There are commercially available systems for recognizing speech and handwriting. Retail companies analyze their past sales data to learn their customers’ behavior to improve customer relationship management. Financial institutions analyze past transactions to predict customers’ credit risks. Robots learn to optimize their behavior to complete a task using minimum resources. These are some of the applications that we will discuss in this lecture. We can only imagine what future applications can be realized using machine learning: Cars that can drive themselves under different road and weather conditions, phones that can translate in real time to and from a foreign language, autonomous robots that can navigate in a new environment, for example, on the surface of another planet. Machine learning is certainly an exciting field to be working in! (from "Introduction to Machine Learning, 3rd Edition, MIT Press, Ethem Alpaydin").

Studiemateriaal

Literatuur

• Ethem Alpaydin, 'Introduction to Machine Learning', MIT Press, 3rd edition

Overig

• Course notes en sheets (op Blackboard)

Leerdoelen

Subjects to be covered: Chapters 1-11 from "Introduction to Machine Learning, 3rd Edition, MIT Press, Ethem Alpaydin", namely: Introduction, Supervised Learning, Bayesian Decision Theory, Parametric Methods, Multivariate Methods, Dimensionality Reduction, Clustering, Nonparametric Methods, Decision Trees, Linear Discrimination, Multilayer Perceptrons.

Onderwijsvormen

• Werkcollege
• Hoorcollege
• Laptopcollege
• Zelfstudie

Verdeling leeractiviteiten

Academische vaardigheden

Learning outcomes: Abstract thinking, understanding formulas

Aanwezigheid

Aanwezigheidseisen opleiding (OER-B):

• Voor practica en werkgroepbijeenkomsten met opdrachten geldt een aanwezigheidsplicht. De invulling van deze aanwezigheidsplicht kan per vak verschillen en staat aangegeven in de studiewijzer. Wanneer studenten niet voldoen aan deze aanwezigheidsplicht kan het onderdeel niet met een voldoende worden afgerond. .
  

Toetsing

Final Grade = 20% of Written Assignment + 20% of Programming Assignment + 25% Mid-Term Exam + 35% Final Exam 

Minimum grade required = 4.5 for each individual component

Opdrachten

6 written assignments + 6 programming assignments

Fraude en plagiaat

Dit vak hanteert de algemene 'Fraude- en plagiaatregeling' van de UvA. Hier wordt nauwkeurig op gecontroleerd. Bij verdenking van fraude of plagiaat wordt de examencommissie van de opleiding ingeschakeld. Zie de Fraude- en plagiaatregeling van de UvA: www.uva.nl/plagiaat

Weekplanning

Rooster

Het rooster van dit vak is in te zien op DataNose.

Aanvullende informatie

Aanbevolen voorkennis: Basiskennis Kunstmatige Intelligentie; programmeren; Statistiek; Lineaire Algebra; Continue Wiskunde en Statistiek.

Contactinformatie

Coördinator

• dr. Zeynep Akata