Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

The focus on the advanced topics during 2020-2022 will be centered around software systems techniques for big data and machine learning.  The study is centered around researching new ideas, evaluating existing techniques, optimizing systems and exploring new solutions. Students will be able to:

  • classify and explain state of the art of systems requirements for big data and ML
  • analyze and apply key metrics and system designs of Big Data/ML applications and services
  • define and develop reliability and performance monitoring and analysis of systems for big data and ML
  • apply and evaluate key programming models and frameworks for Big Data/ML
  • produce and evaluate edge system designs for Big Data and ML
  • present and discuss new hardware architectures and quantum for Big Data/ML
  • present and discuss future perspectives on systems for Big Data/ML

Credits: 5

Schedule: 15.09.2021 - 08.12.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Linh Truong

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • valid for whole curriculum period:

    The course of Advanced Topics in Software Systems has selected topics, being updated yearly. For the period 2020-2022, the course will focus on the following areas:

     

    • Design and evaluation for systems robustness, reliability, resilience and elasticity for Big Data/ML (with also engineering work)
    • Test, debug, monitoring, and configuration management (with also engineering work)
    • Dataflows and orchestration frameworks for Big Data/ML (with also engineering work)
    • Edge systems and edge-cloud continuum for Big Data/ML (with also engineering work)
    • New hardware architectures and quantum systems for Big Data/ML (more on the concepts and state-of-the-art)

     

Assessment Methods and Criteria
  • valid for whole curriculum period:

    The assessment of the course will be based on the follow activities:

    • study log and contributing topic identification, pass with >=50% positive
    • study log and assessment of other work, pass with >= 50% positive
    •  

    Workload
    • valid for whole curriculum period:

      Small group teaching (20), seminar (16), and individual work and demonstration (99)

      • Lectures and discussions: 10
      • Reflection on lectures and discussion: 10
      • Participation on peer work and reflection: 16
      • Individual topic identification: 24
      • Individual study and presentation: 35
      • Individual project and demo: 40

    DETAILS

    Study Material
    • valid for whole curriculum period:

      lectures, reading list, open sources, hands-on problems

    Substitutes for Courses
    Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Workload might be shifted between different categories (lectures, hands-on, individual work) accordingly to specific topics/areas selected.

    Teaching Period:

    2020-2021 Autumn I-II

    2021-2022 Autumn I-II

    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CS-E4660

    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

    Course registration will be approved based on pre-assignment, interview or prerequisite check.