ACIT4530 Data Mining at Scale: Algorithms and Systems Course description

On completion of this course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The course will give the students in depth knowledge on the following topics

• Sampling, digitalization and reconstruction.
• How to obtain the signal spectrum of digital and analog signals
• How to obtain the frequency response of digital systems
• Methods of filtering discrete signals
• Digital image formats

Skills

The student will know how to:

• Describe digital signals and systems mathematically in the time domain
• Describe discrete signals and systems in the frequency domain and the Z-domain
• Describe digital images mathematically in real space and frequency space
• Describe linear time invariant systems using difference equations, impulse responses, and transfer functions
• Analyze time discrete systems in the frequency domain
• Use discrete filters and the digitized versions of analog filters: FIR and IIR
• Apply post processing of images for filtering, noise reduction, edge detection and presentation.

General competence

The student will have general competence on:

• Frequency spectrums, impulse responses, frequency responses, convolution and modulation
• Fourier-series (FS), Fourier transform (FT).
• Sampling, reconstruction and aliasing
• Implementation of DSP-filters
• Improving image presentation

It is an advantage to have some experience with the following subjects:

• Mathematical Analysis
• Basic programming, such as scripting
• Statistics, specifically probability theory

Lectures.

Exercises.

Computer exercises.

None

This course is divided into two parts. The first part with focus on covering the principles of data mining and stream processing. Different seminars will be given on the different methodological aspects of data mining and stream processing as well as the programming paradigms and software tools that enable them.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work in a group on the project and submit a final code-base with a report.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

All written material, but no communication will be allowed.

Group project (2-4 students) between 15 000 and 17 500 words

The exam can be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

Two internal examiners. External examiner is used periodically.

Associate Professor Nils Sponheim

Signal Processing or knowledge of the Fourier and Laplace transforms. Some knowledge of Matlab progamming.

This course will contain the following topics:

• Sampling, reconstruction and aliasing
• Impulse response and difference equations
• Fourier series and Fourier transform
• Frequency analysis and frequency response
• Transfer functions, filter design of FIR and IIR filters
• Image presentation and processing