Text

Course syllabus - Machine Learning With Big Data

Scope

7.5 credits

Course code

DVA453

Valid from

Autumn semester 2019

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements).

Main area(s)

Computer Science

School

School of Innovation, Design and Engineering

Ratified

2017-01-31

Revised

2019-01-25

Literature lists

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

    • Ratified date: 2019-06-24
    • Latest update: 2019-06-24

    Books

    Shalev-Shwartz, Shai; Ben-David, Shai

    Understanding machine learning : from theory to algorithms

    New York, NY : Cambridge University Press, 2014 - xvi, 397 p.

    ISBN: 978-1-107-05713-5 LIBRIS-ID: 16717946

    Abu-Mostafa

    Learning From Data

    ISBN: 9781600490064 LIBRIS-ID: 13914157

    James, Gareth.; Witten, Daniela.; Hastie, Trevor.; Tibshirani, Robert.

    An Introduction to Statistical Learning : with Applications in R

    New York, NY : Springer New York, 2013. - XIV, 426 p. 150 illus., 146 illus. in color.

    ISBN: 978-1-4614-7138-7 LIBRIS-ID: 14557777

    URL: Table of Contents / Abstracts

    Richter, Michael M Author

    Case-Based Reasoning A Textbook

    LIBRIS-ID: 21159926

    Guyon, Isabelle

    Feature extraction [electronic resource] : foundations and applications / Isabelle Guyon ... [et al.] (eds.).

    Springer-Verlag,

    LIBRIS-ID: 22022307

    Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron

    Deep learning

    Cambridge, MA : MIT Press, [2016] - xxii, 775 pages

    ISBN: 978-0-262-03561-3 LIBRIS-ID: 19973915

    Yu, Shui.; Guo, Song.

    Big Data Concepts, Theories, and Applications

    1st ed. 2016. : Cham : Springer International Publishing, 2016. - VIII, 437 p. 97 illus., 17 illus. in color.

    ISBN: 978-3-319-27763-9 LIBRIS-ID: 19419072

    URL: Table of Contents / Abstracts

    Karau, Holden Author

    Learning Spark : Lightning-Fast Big Data Analytics [electronic resource]

    LIBRIS-ID: 21399966

    Guido, Sarah

    Introduction to Machine Learning with Python

    O'Reilly Media, 2016 - 400 s.

    ISBN: 9781449369415 LIBRIS-ID: 17554477

    URL: Bibliotekets e-bok/Library e-book

    Raschka, Sebastian

    Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analysis

    Birmingham : Packt Publishing Limited, 2015 - 425 s.

    ISBN: 1-78355-513-0 LIBRIS-ID: 18646485

    Provost, Foster Author

    Data Science for Business : What You Need to Know about Data Mining and Data-analytic Thinking [electronic resource]

    LIBRIS-ID: 21842654

    URL: Bibliotekets e-bok/ Library e-book

    Articles

    Tsai, C. W.; Lai, C.F.; Chao, H.C.; Vasilakos, A.V.

    Big data analytics: a survey

    Journal of Big Data, vol. 2, no. 1, 2015

    Landset, S.; Khoshgoftaar, T.M.; Richter, A.N.; Hasanin, T.

    A survey of open source tools for machine learning with big data in the Hadoop ecosystem

    Journal of Big Data, vol. 2, no. 1, 2015

    Fan, J.; Han, F.; Liu, H.

    Challenges of Big Data analysis

    National Science Review, vol. 1, no. 2, 2014

    Blum, A.L.; Langley, P.

    Selection of relevant features and examples in machine learning

    Artificial Intelligence, vol. 97, no. 1–2, 1997

    Dash, M.; Liu, H.

    Feature selection for classification

    Intelligent Data Analysis, vol. 1, no. 1–4, 1997

    Aamodt, A.; Plaza, E.

    Case-based reasoning: foundational issues, methodological variations, and system approaches

    AI Commun., vol. 7, no. 1, 1994

    Lopez De Mantaras, R.; et. al

    Retrieval, reuse, revision and retention in case-based reasoning

    The Knowledge Engineering Review, vol. 20, no. 03, 2005

    Meng, X.; et. al

    MLlib: machine learning in apache spark

    J. Mach. Learn. Res., vol. 17, no. 1, 2016

Objectives

The rapid development of digital technologies and advances in communications have led to gigantic amounts of data with complex structures called 'Big data' being produced every day at exponential growth. The aim of this course is to give the student insights in fundamental concepts of machine learning with big data as well as recent research trends in the domain. The student will learn about problems and industrial challenges through domain-based case studies. Furthermore, the student will learn to use tools to develop systems using machine-learning algorithms in big data.

Learning outcomes

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

1. describe the basic principles of machine learning and big data
2. demonstrate the ability to identify key challenges to use big data with machine learning
3. show the ability to select suitable Machine Learning algorithms to solve a given problem for big data
4. demonstrate the ability to use tools for big data analytics and present the analysis result

Course content

Module 1. Introduction and background: introduction is intended to review Machine Learning (ML) and Big Data processing techniques and related subtopics with focus on the underlying themes.
Module 2. Case studies: presents case studies from different application domains and discuss key technical issues e.g., noise handling, feature extraction, selection, and learning algorithms in developing such systems.
Module 3. Machine learning techniques in big data analytics: this module consists of basic understanding of learning theory, clustering analysis, deep learning and other classification techniques appropriate for development work and issues in construction of systems using Big Data.
Module 4.  Data analytics with tools: presents open source tools e.g., KNIME and Spark with examples that guide through the basic analysis of Big Data.

Specific requirements

90 credits of which at least 60 credits in Computer Science or equivalent, including at least 15 credits in programming. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

Examination

Written assignment (INL1), (Module 1), 1,0 credit, (examines the learning outcome 1), marks Fail (U) or Pass (G)
Written assignment (INL2), (Module 2), 1,5 credits, (examines the learning outcome 2), marks Fail (U) or Pass (G)
Written assignment (INL3), (Module 3), 2,0 credits, (examines the learning outcome 3), marks Fail (U) or Pass (G)
Project (PRO1), (Module 4), 3 credits, (examines the learning outcome 4), 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