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

This course focuses on advanced topics in software systems. The topics will be centered around software systems techniques for edge-cloud continum, distributed and end-to-end machine learning, and new continuum computing models, such as cloud-hpc-quantum integration.  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 in edge-cloud continuum
  • 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 systems
  • 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: 04.09.2024 - 11.12.2024

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 2024-2026, the course will focus on the following areas:

    • Quality of analytics for end-to-end ML systems and services
    • Design and evaluation for systems robustness, reliability, resilience and elasticity for Big Data and distributed/realtime 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
    • a selected topic and its presentation of  a concept, design principles, tools, pass with >= 50% positive
    • identificaton of  an open problem and the project work for the problem with in-depth technical design, prototype and/evaluation, pass with >=50% positive, excellent if the result is novel.

    A student will pass the course if the student has 4 passes and will pass with excellent if the last activity leads to some novel results. Note that in Aalto officially we have only pass/fail. The teacher will find a suitable way to recognize "pass with excellent" (e.g., mark a note or give a certificate issued by the professor personally).

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
SDG: Sustainable Development Goals

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

    Registration:

    Maximum number of students: 16. Course registration will be approved based on pre-assignment, interview or prerequisite check.