ACIT4630 Advanced Machine Learning and Deep Learning Emneplan

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.

• Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
• Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

No formal requirements over and above the admission requirements.

This course offers an overview and adopts a critical perspective of research into different types of intervention in the health sciences related to different levels, arenas, population groups and/or individuals. Interventions in health sciences are often complex. The course focuses on methodology in interventions in the areas of health-promoting, preventive, rehabilitative, curative, and palliative measures. The course takes a systematic approach and places emphasis on the process of developing, planning, implementing, and evaluating the effects of interventions. Different types of research design in intervention research will also be discussed.

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On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

Knowledge

The PhD candidate

• is at the forefront of knowledge of conceptual understanding and of theories related to research into different types of health interventions
• can evaluate the usefulness and application of different designs, methods, and processes in research related to health interventions
• can contribute towards developing new knowledge, theories, methods, and interpretations of positive and negative results of health interventions
• can evaluate the usefulness and application of relevant guidelines, best practice reports, and different types of systematic review

Skills

The PhD candidate can

• address complex issues related to developing, planning, and evaluating interventions
• address issues related to the connection between the theoretical basis of interventions, measures, and evaluation
• address complex issues related to analytical and ethical considerations in health intervention research and is able to challenge established knowledge and practice in the field

General competence

The PhD candidate can

• present and discuss knowledge about intervention in the health sciences
• can assess the need for, and initiate, innovations in intervention

Work and teaching methods consist of lectures, seminars, and self-study. The outcomes of the seminars are presented and discussed in plenary sessions.

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Candidates must write an essay based on a problem of their choice and discuss theoretical and research challenges associated with planning an intervention. The essay must consist of up to 5,000 words and must be submitted no more than 2 weeks after the end of the course.

All

Pass / Fail