LEARNING OUTCOMES
The focus on the advanced topics during 2022-2024 will be centered around software systems techniques for big data, distributed machine learning, and new continuum computing models, such as edge-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
- 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: 06.09.2023 - 05.12.2023
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 2022-2024, the course will focus on the following areas:
- 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
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn I - II
2023-2024 Autumn I - IIEnrollment :
Registration for Courses: registration for courses will take place on Sisu (sisu.aalto.fi). Course registration will be approved based on pre-assignment, interview or prerequisite check.
- Teacher: Truong Linh