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EP2420 Network Analytics 7.5 credits

Administer About course

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. 

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Application code

50491

Headings with content from the Course syllabus EP2420 (Spring 2019–) are denoted with an asterisk ( )

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 

Literature and preparations

Specific prerequisites

For single course students: 120 credits and documented proficiency in English B or equivalent

Recommended prerequisites

Basic knowledge in statistics, machine learning, networking, and computing systems. The projects require programming in Python.

Equipment

Students are assumed to have access to computers.

Literature

Chapters from textbooks, online material, research papers will selected according to the specific project and will be made made available to students.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F

Examination

  • 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. 

Other requirements for final grade

Requirements for passing the course are that the student successfully completes both course projects and passes an assessment interview.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Rolf Stadler

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.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Computer Science

Main field of study

Electrical Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

Course documentation and background literature will be available through the course web site.

Replacing EP2400 Network Algorithms.

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex.