PENG9560 Topics in Artificial Intelligence and Machine Learning Emneplan

This course covers advanced topics in artificial intelligence and machine learning, both theory and practice, recent scientific papers and state-of-the-art techniques.The course will be offered once a year, provided 3 or more students sign up for the course. If less than 3 students sign up for a course, the course will be cancelled for that year.

Lectures, workshops, cases, presentations, project. 

Students who complete the course are expected to have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of the course, the student has:

• an in-depth understanding of machine learning in its main forms: supervised, unsupervised, and reinforcement learning, both theoretical and applied, to solve real- lifeproblems.
• knowledge and understanding of the main concepts of deep learning.
• knowledge and understanding of some major concepts in artificial intelligence, including: complex systems (network models, cellular automata, and agent-based models) and evolutionary computing.

Skills

On successful completion of the course, the student can:

• apply techniques from machine learning to real-life problems.
• analyse data sets with the aid of machine learning algorithms.

General competence

On successful completion of the course, the student can:

• use libraries for programming deep learning algorithms such as TensorFlow.
• deploy models; to relevant real-life problems.
• solve computational problem using evolutionary computing.

Each module will be taught in a series of lectures. At the end of each module, the students will be assigned a small project to be submitted within a given deadline.

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

Compulsory assignments must be approved prior to the exam. The students must submit a small project at the end of each module. All five projects must be approved before examination.

Oral examination, individual.

The exam cannot be appealed.

This course aims to enable students to understand the research process in the broad field of management and the specific requirements of entrepreneurship;and;innovation research. The course provides opportunities for students to plan, carry out and communicate research projects for their project work as well as for their master’s thesis. In particular, the course aims to cover current qualitative research designs and tools for innovators and entrepreneurs as well as knowledge about how to conduct segmentation based on big data appropriate for a digital age. Particular attention is given;to participant-oriented and practice-oriented research such as action research, phenomenology, ethnography, grounded theory, qualitative experiments and narrative methods. Case study as an overarching research design that enables bridging theory and practice. The course goes back to the classics in qualitative theory and moves on to applications in innovation and entrepreneurship research.

None

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:;

Knowledge ;

The student has in-depth knowledge of ;

• how phenomena and practices are refined and investigated scientifically using qualitative methods;
• the difference between natural, field and laboratory settings ;
• how research in collaboration with customers/users/practitioners/stakeholders is established,;developed, and reported;
• purposive sampling strategies and recruitment in qualitative research;
• principles of action research methods, phenomenology, ethnography, grounded theory, qualitative experiments, case studies, critical incidents technique, narrative methods,;mixed methods and naturally occurring data;
• how to theorize from process data as opposed to deductive analysis ;
• ethics and relational research
• market segmentation utilising surplus digital data ;

Skills ;

The student has ;

• the ability to apply;qualitative data from existing databases ;
• the ability;to compare and select different research methods ;
• the ability;to design, implement and evaluate qualitative research projects
• confidence in generating data and discuss the results in cooperation with customers/users/practitioners/stakeholders in research projects ;
• the ability to go behind the stated and understand challenges of everyday life ;
• the ability to use qualitative designs in research and development projects in an independent way ;
• the ability to plan, collect, systemize, analyze and interpret qualitative data from composite contexts ;
• confidence in communicating the empirical findings to different target groups, the general public included ;
• the ability to apply big data sets for segmentation ;

General competence ;

The student ;

• has the ability to;build trust in relationships with individuals and stakeholders in R&D projects
• understands;ethical principles for participant-oriented and practice-oriented research
• has the ability to reflect on;his or her;own research position ;
• has a critical and creative attitude to method selection across sectors, industries and tasks ;
• has acquired systematic and analytical capabilities, and has strengthened the ability to solve problems
• can communicate relevant professional issues, analyses and conclusions in fields bordering innovation and entrepreneurship ;
• has the ability to;secure research quality;