Syllabus

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

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  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.

Description