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Course presentation
This project course introduces students to data-driven engineering of networks and cloud systems. Using methods from statistical learning, students will develop and evaluate, for instance, models for prediction and forecasting of Key Performance Indicators (KPIs) and for anomaly detection. The models will be fitted and evaluated using testbed measurements or traces from operational systems. The functions built from these models are designed for real-time execution.
To develop the models, tools and packages from data science will be used, e.g., Jupyter notebook, scikit-learn, TensorFlow.
The course is structured as two consecutive project blocks. Each block starts with introductory lectures that give background and discuss concepts for the specific project, followed by project execution, writing of a short report, and interview.
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019
Content and learning outcomes
Course contents
This is a project course whereby students, by themselves or in small groups, perform an analytics project using data from a real system, for instance, using operational data from a network or compute cloud.
The course includes:
- introductory lectures on the specific machine-learning techniques used in the project
- an introduction into the tools to be used
- execution of the project by students, supported by message board and project meetings
- preparation of the project report by students
The specific project the students work on can change from year to year.
Intended learning outcomes
After passing this course, participants should be able to:
- perform the modeling of a network analytics task
- pre-process data and create predictive models using machine-learning techniques and tools
- assess, interpret and possibly apply the results
- produce a written report describing and explaining the project results
Basic knowledge in statistics, machine learning, networking, and computing systems. The projects require programming in Python.
Specific preparations
In order to carry out the projects, you need basic knowledge in statistics, machine learning, networking, and computing systems.
The more you know about machine learning, the better. If you have only little knowledge, we give you information to catch up for the course, but you will have to work harder to pass the course.
The projects require programming in Python. We will use the Anaconda data science platform. Basic programing skills will be sufficient for the course. However, If you have more programing experience, you can focus on the task at hand instead of on programming issues.
Literature
We will provide access to lecture notes and literature through CANVAS.
Equipment
You will be able to perform a part of the project work on your laptop. We will give you access to compute servers for the more intensive tasks.
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
PRO1 - Project Work, 7.5 credits, Grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Grading will be based on the on the project report and the project interview.
The report and the interview will have equal weight for the grade.
In the project report, the project results will be evaluated in terms of correctness and completeness, and the presentation will be evaluated regarding structure and readability.
In the interview, students will be evaluated for their understanding of the project objective, approach, and results.
Grade A means the student has executed the complete project, has obtained correct results, and has produced a readable and concise report. Further, the student has answered well to all interview questions.
To pass the course, the student has executed the complete project, has obtained correct results for significant project parts, has produced a readable report, and has answered correctly to most interview questions.
The section below is not retrieved from the course syllabus:
Project Work ( PRO1 )
Other requirements for final grade
Requirements for passing the course are that the student successfully completes both course projects and passes an assessment interview.
Ethical approach
All members of a group are responsible for the group's work.
In any assessment, every student shall honestly disclose any help received and sources used.
In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
