Leren

Learning

Studiewijzer 2024/2025

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

Overig

• Course notes, recommended literature and slides on Canvas

Leerdoelen

• The student can explain the primary goal of machine learning, the fundamental methods by which this is achieved, and the inherent limitations.
• The student can analyse a machine learning task and compare and contrast it in terms of e.g. unsupervised or supervised, regression or classification, etc.
• The student can apply foundational machine learning algorithms to discrete and/or continuous data and interpret the result.
• The student can analyse the fit of a learned model, diagnosing underfitting or overtfitting, hyperparameter sensitivity, generalization etc., then use these results to tune and improve the model.
• The student can evaluate a complete machine learning pipeline (including data collection, data processing, model application and validation), and choose an approach that can improve the result.

Onderwijsvormen

• Werkcollege
• Hoorcollege
• Laptopcollege
• Zelfstudie

Verdeling leeractiviteiten

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.
  

Aanvullende eisen voor dit vak:

Het wordt sterk aanbevolen om naar de laptop- en werkcolleges te gaan, maar er is geen aanwezigheidsplicht.

Toetsing

There will be weekly assignments (written and programming) during the course. The course will conclude with one mid-term exam, one final exam and one retake exam.

Minimum required grade for the assignments is 5, if the student fails to obtain a minimum of 5 from the assignments (written and programming together), the student will fail the course.
Minimum required grade for the exams is 5, if the student fails to obtain a minimum of 5 (mid-term and final together), the student will fail the course.

The grade will be computed as follows:

Total = 0.3*mid-term + 0.4*final + 0.15*written + 0.15*programming

If the student fails, the retake exam will affect the grade as follows:

Total = 0.7*retake + 0.15*written + 0.15*programming

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: http://student.uva.nl

Weekplanning

Aanvullende informatie

Recommended prior knowledge: having successfully completed a. BSc level Linear Algebra course as well as a BSc level Statistics course is a requirement. Without basic math and statistics knowledge this course can not be successfully completed. 

Course is taught in English

Contactinformatie

Coördinator

• Christian Andersson Naesseth

Coördinator

• dr. Christian A. Naesseth

Docenten

• G. Bartosh MSc
• Julian Blaauboer
• Rens den Braber
• Mara Fennema BSc
• Dionne Gantzert BSc
• Metod Jazbec MSc
• Gerson de Kleuver BSc
• Thijs Kuipers BSc
• Derck Prinzhorn
• Mona Schirmer MSc
• Bas Terwijn
• Noa Visser MSc