VERB2200 Behavior Analysis - from Theory to Application Course description

The main focus of the course is to learn how behaviour is established, changed, and maintained to increase independence and improve quality-of-life. The students will learn the basic principles of behaviour analysis and how behaviour is operationalised and measured. The course also introduces experimental methodology with an emphasis on single-subject designs to allow students to assess the effect of behavior analytic interventions and understand research results in the field. Self-determination, social validity and the legal basis for services provided are emphasized. Different schools of thought in the field of psychology are compared. Relevant philosophy of science and ethics are presented, along with the historical development of applied behaviour analysis.

The course is taught over ten weeks, including one week for Interact.

Passed VERB1100, VERB1600, VERB1300, VERB1400, VERB1510 and VERB1210.

On successful completion of the course, the student has the following learning outcomes classified as knowledge, skills and general competence:

Knowledge

The student

• can describe the main features of the historical development of applied behaviour analysis
• can explain how philosophy of science is relevant to the practice of applied behaviour analysis
• can explain basic principles of behaviour analysis
• can describe different ways of observing and measuring behaviour
• can discuss self-determination, supported decision-making and social validity
• can describe different single-subject experimental designs and selected research methods that can be used in social education
• can describe characteristics of empirically supported interventions

Skills

The student

• can assess similarities and differences between important theories in the disciplines of psychology and pedagogy
• can assess social validity of interventions based on behaviour analytic principles
• can operationalise and analyse behaviour
• can assess the effect of interventions by analysing figures that present single-subject experimental designs
• can apply basic legal methods to implement lawful and professional practice
• can assess how welfare technology can be introduced to support the individual's resources and possibilities for mastery of functional skills

General competence

The student

• can apply behaviour analytic principles to promote self-determination and equal participation in society in line with applicable legislation, professional guidelines and ethical considerations
• can assess the effect of interventions based on behaviour analytic principles to promote socially valid behaviour change
• can apply relevant and up-to-date specialist literature to shed light on practical problems, make well-founded choices and apply behaviour analytic principles in socially valid behavior change
• can assess and discuss the use of digital tools and AI in professional work, including ethical and legal implications

Quantum information technology implements quantum phenomena to process information and communicate it beyond the limits of the classical world. According to the EU Quantum Technologies Flagship report, such technology is based on the following pillars:

• Quantum computation
• Quantum communication
• Quantum simulation
• Quantum metrology and sensing

This course will introduce students to the first three of these fields, by equipping them with knowledge of principles, ideas, and methods. Many of these methods are also applicable within several other fields.

Prior knowledge in quantum physics is not required. The first few weeks of the course is dedicated to an introduction to key concepts in quantum physics. These concepts are introduced in a practical manner - with emphasis on simulation and phenomenology rather than theory.

The students will be trained to create their own quantum algorithms, simulate quantum systems, and implement the corresponding programs on classical and quantum computers. By implementing calculations and simulations of quantum systems, the students will learn about the fundamental quantum phenomena and key concepts. Moreover, in order to lay the proper foundation, the fundamental concepts of classical information theory is introduced.

A selection of recently published quantum algorithms and methods, including communication protocols, computational methods of modern quantum physics, and optimization algorithms, will be presented and analysed. Particular focus will be given to applications in data science in order to address research challenges in sustainable systems. Finally, the most recent challenges and particular proof of concept problems, including so-called quantum supremacy, will be addressed.

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

• 100% attendance in supervised group work in groups of six students
• completed multiple-choice test by the given deadline
• Individual written paper, up to 1,000 words. Feedback on content in accordance with specified criteria

Individual written examination with invigilation, 4 hours

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course the student

• is familiar with fundamental key concepts within information theory such as Shannon Entropy, noiseless and noisy-channel coding theorems, and optimal coding algorithms.
• knows what a qubit is and how the information content grows when qubits are connected.
• is familiar with the elementary operations, or gates, of quantum computing - including gates such as the Hadamard gate and CNOT.
• knows the present state of the art when it comes to existing quantum computers.
• can implement simple quantum algorithms and run them on actual quantum computers.
• knows basic quantum communication protocols such as key distributions and secret sharing and understands the ideas behind them
• is familiar with several methods, such as Shor’s algorithm and quantum annealing, which enables quantum computers to solve problems considerably faster than classical computers.
• is familiar with how quantum technology affects traditional encryption schemes, and provides novel ones.

Skills

On successful completion of the course the student

• is able to model and simulate numerically simple quantum systems and processes - both on classical and quantum computers.can independently devise, implement and run calculations and simulations of simple quantum systems.
• can design her/his own quantum algorithms.

General competence

On successful completion of the course the student

• is familiar with several phenomena specific to quantum physics - such as quantization, particle interference, collapse of the wave function, particle spin, entanglement and decoherence - and how they may manifest themselves within quantum computing.
• is familiar with how information may be described by quantitative means - both within a classical and a quantum context.
• knows how to revise and improve on implementations of quantum programs.
• can address some of the practical challenges related to building quantum computers.
• knows the importance of quantum computing within information technology and the open challenges yet to be solved in this scope.

The teaching is organized in sessions where the subject material is presented, and in sessions where the students solve problems on their laptops and prototype quantum computers. The latter is done using online cloud platforms currently provided by enterprises such as, e.g., IBM and D-Wave. Between these sessions, the students are expected to work independently, using their computers, access to quantum computers, and course notes.

In the last stage of the course, the students are required to complete and present an individual project that involves (i) simulation of a quantum system/process, (ii) simulation of a quantum communications protocol, or (iii) creation of a quantum code and its implementation on a quantum processor using an online cloud platform. The project should be concluded by submitting a report which provides a description of the project, its motivation and implementation, and an analysis the obtained results.

None

The assessment will be based on a portfolio of the following:

• One individual project delivery consisting of a report (2000 - 4000 words)
• An individual oral examination (30 minutes)

The portfolio will be assessed as a whole and cannot 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.

In the event of a postponed examination in this course the exam may be held as an oral exam. Oral exams cannot be appealed.