Machine Learning 1

6 EC

Semester 1, period 2

52041MAL6Y

Owner Master Artificial Intelligence
Coordinator dr. Rianne van den Berg
Part of Master Artificial Intelligence, year 1Master Computational Science (Joint Degree), year 1

Course manual 2018/2019

Course content

This course is lecture based, with homework assignments and programming.

The curriculum is based on 1,2,3,4,5,6,7,9,14 chapters of the book "Pattern Recognition and Machine Learning" by C. Bishop:

  • · Bayesian Decision Theory
  • · Linear regression
  • · Linear classification
  • · Neural networks
  • · Kernel methods
  • · Dimensionality reduction
  • · Clustering methods
  • · Ensemble methods

Study materials

Literature

  • C.M. Bishop, 'Pattern Recognition and Machine Learning', 2006, Springer, ISBN 0-38-731073-8

Objectives

  • Gain a deep understanding of the principles of machine learning and pattern recognition.
  • Acquire the skills to apply machine learning to real world problems.

Machine learning is concerned with learning predictive algorithms from data. In this course you will learn about supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction). Special attention will be paid to statistically analyzing the results of applying an algorithm to a particular problem. You will learn the theory of machine learning in class and practice the theory during homework sessions. You will gain hands-on experience through a number of coding projects where you implement some of the algorithms.

Teaching methods

  • Lecture
  • Laptop seminar
  • Computer lab session/practical training
  • Seminar

Lectures, homework, and computer lab sessions.


Learning activities

Activity

Number of hours

Hoorcollege

28

Laptopcollege

14

Tentamen

3

Tussentoets

2

Werkcollege

24

Zelfstudie

97

Attendance

The programme does not have requirements concerning attendance (OER-B).

Assessment

Item and weight Details

Final grade

0.6 (60%)

Tentamen

0.2 (20%)

Homework assignments

0.2 (20%)

Programming assignments

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1
2
3
4
5
6
7
8

Timetable

The schedule for this course is published on DataNose.

Additional information

Recommended prior knowledge: Calculus, Linear algebra, probability theory, statistics, programming.

Contact information

Coordinator

  • dr. Rianne van den Berg