ACIT4510 Statistical Learning Emneplan

The course covers the foundations and recent advances in Machine Learning from the point of view of Statistical Learning Theory. The goal of this course is to provide students with the practical skills to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

Two internal examiners. External examiner is used periodically.

Signal processing defines the mathematical tools used to analyze, model and operate on measured data from physical signals and their sources. In digital signal processing (DSP) the following topics are lectured: the different domains for describing discrete signals and linear time invariant systems, digital filters and frequency analysis. Images are two-dimensional signals and similar analytic methods apply.

No formal requirements over and above the admission requirements.

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

Lectures.

Exercises.

Computer exercises.

None

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

Grade scale A-F.

Two internal examiners. External examiner is used periodically.

Professor Pedro Lind