Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), 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: 09.09.2020 - 02.12.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Linh Truong
Teacher in charge (applies in this implementation): Linh Truong
Contact information for the course (valid 11.08.2020-21.12.2112):
Contact the responsible teacher:
- Hong-Linh Truong: linh.truong@aalto.fi
Join the teams space for the course:
Also check CS-E4640 MyCourses and CS-E4660 GIT space for further information.
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
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 01.08.2020-31.07.2022:
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 01.08.2020-31.07.2022:
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
Applies in this implementation:
The current workload (adjusted for this semester) is
- students will participate in 4 lectures + 4 hands-on: in total 16 hours for participation and discussion
- students will spend 16 hours for reflections and self-study of lectures and hands-on
- each student should spend 24 hours on identifying course individual topic
- each student should spend 35 hours for studying the individual topic, present and discuss the topic
- each student should spend 40 hours to implement the individual topic and demonstrate the work in the course.
- students will participate in 4 lectures + 4 hands-on: in total 16 hours for participation and discussion
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
lectures, reading list, open sources, hands-on problems
Applies in this implementation:
Course materials are available in
- PDFs of lectures
- hands-on tutorial materials
- Reading list
All can be accessed from MyCourses and CS-E4640 GIT. Some materials from the previous sessions are also available.
Prerequisites
Valid 01.08.2020-31.07.2022:
Students should have basic knowledge about software systems, distributed computing and software development, e.g., cloud computing, big data, operating systems, distributed systems and machine learning. Therefore, it is important that students have passed courses with these topics, such as "Cloud Computing", "Big Data Platforms", "Operating Systems", and "Machine Learning". Students who have not completed such courses can still join the course after having the interview with the teacher in charge. Note that prerequite might change a bit each year as the topics discussed in the course are not the same every year.
Registration for Courses
Valid 01.08.2020-31.07.2022:
Course registration will be approved based on pre-assignment, interview or prerequisite check.
Applies in this implementation:
Students should consider this is an advanced course to learn and research emerging topics.
FURTHER INFORMATION
Further Information
Valid 01.08.2020-31.07.2022:
Workload might be shifted between different categories (lectures, hands-on, individual work) accordingly to specific topics/areas selected.
Applies in this implementation:
Students should consider this is an advanced course to learn and research emerging topics.
- Teacher: Nguyen Tri
- Teacher: Pham Phuong
- Teacher: Truong Linh