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Course syllabus - Introduction to Machine Learning

Scope

2 credits

Course code

DVA133

Valid from

Autumn semester 2022

Education level

First cycle

Progressive Specialisation

G1N (First cycle, has only upper-secondary level entry requirements).

Main area(s)

Computer Science

School

School of Innovation, Design and Engineering

Ratified

2020-01-24

Revised

2022-01-24

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2022-11-17
    • Latest update: 2022-11-17

    Reference Literature

    Aggarwal, Charu C.

    Data Mining : The Textbook

    Cham : Springer International Publishing, 2015 - XXIX, 734 p. 180 illus., 7 illus. in color.

    ISBN: 9783319141428 LIBRIS-ID: 17970264

    URL: Table of Contents / Abstracts

    K. F. Riley. M. P. Hobson. S. J. Bence.

    Mathematical methods for physics and engineering : a comprehensive guide, third edition

    Cambridge University Press, 2006

    LIBRIS-ID: 18540907

    Hetland, Magnus Lie.

    Python Algorithms : Mastering Basic Algorithms in the Python Language

    Second Edition. : Berkeley, CA : Apress, 2014 - XVI, 320 p. 76 illus.

    ISBN: 9781484200551 LIBRIS-ID: 17089959

    URL: Table of Contents / Abstracts

    Other Materials

    Övrigt referensmaterial presenteras under kursens gång.

    Other reference materials will be presented during the course.

Objectives

The aim of the course is to provide fundamentals of machine learning as well as introduce basic concepts of data manipulation and processing, mathematics, statistics and probability insofar they are related to machine learning.

Learning outcomes

After completing the course, the student shall be able to:

1. describe fundamentals of machine learning,
2. understand the basic of data manipulation and processing and also
3. perform basic mathematical and statistical operations for machine learning using Python.

Course content

The course content covers:

- Fundamentals of machine learning: Overview, AI components, branches, goals, types, algorithms, and applications. Basic mathematical background for machine learning using Python.

 - Introduction to data: Data, data manipulation, processing and visualization. Basics of statistics and probability required to process data.

Requirements

Basic eligibility and Mathematics 3b or 3c or Mathematics C

Examination

Written assignment (INL1), 2 credits, examines the learning objectives 1-3, marks Fail (U), or Pass (G).

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Study guide

Grade

Two-grade scale