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
- 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:
Prerequisites
valid for whole curriculum period:
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.