ACIT4630 Advanced Machine Learning and Deep Learning Course description

This course provides a broad introduction to machine learning (ML), which includes supervised, unsupervised, and reinforcement learning, and deep learning (DL) that can be used in different application domains. Students will learn both theories and practices in ML and DL. Moreover, students will learn from studying, presenting, and discussing relevant research articles and expose themselves to research by doing a research project.

Aids autumn 2020:

All aids allowed, except communication with others

[Aids earlier:]

Calculator that cannot be used to communicate with others.

No formal requirements over and above the admission requirements.

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as convolutional neural networks, sequential models (RNN, LSTM), Transformer, Generative models (Autoencoder, GAN), and reinforcement learning.
• has a good understanding of both theoretical and practical know-how required to use machine learning and deep learning methods effectively

Skills

The student can:

• build, train, test, and deploy machine learning and deep learning models
• analyze machine learning methods in regard to their performance and effectiveness
• use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine learning and deep learning methods
• can discuss relevance, strength, and limitations of machine learning and deep learning in solving real-world problems
• can work on effectively relevant research projects

The course consists of lectures, assignments, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.

The following required coursework must be approved before the student can take the exam:

• Two oral presentations (one on a given topic, one on the topic of own choice)
• Participate as a prepared opponent/discussant in two presentations from other students

There is a minimum 80% mandatory attendance in this course. Students who do not meet this requirement will not be allowed to sit the exam.

Exam in two parts:

• A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course.
• Individual oral exam (about 30 minutes).

Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.

Both exams must be passed in order to pass the course.

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.

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 and analogue systems
• Filtering
• Detection techniques, correlation and deconvolution
• Reconstruction of images from measured data by inverse methods

Skills

The student will know how to:

• Describe digital signals and systems mathematically in the time domain, the frequency domain and the Z-domain
• Describe digital images mathematically in real space and K-space
• Describe linear time invariant systems using difference equations, impulse responses, point spread functions and transfer functions
• Analyze time discrete systems in the frequency domain and discrete images in K- space
• Use analog filters and the digitized versions of analog filters: FIR and IIR
• Apply post processing of images for filtering
• Apply inverse methods for image reconstruction from measured data

General competence

The student will have general competence on:

• Spectrums, impulse responses, point spread functions, frequency responses, K- space, correlation, convolution and modulation
• Fourier-series (FS), Fourier transform (FT).
• Sampling, reconstruction and aliasing
• Implementation of DSP-filters and inverse methods on a computer.

Lectures.

Practical training

Computer exercises.

Mandatory computer exercises, individual or in groups (6 hours per week).

Exam autumn 2020 due to Covid-19:

Individual digital home exam, 3 hours.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.

[Exam earlier:]

Individual written exam, 3 hours.

The exam grade can be appealed.

In the event of a resit or rescheduled exam, an oral examination may be used instead. In such case, the grade cannot be appealed.