Undergraduate Course: Brain, Cognition and Artificial Intelligence 4 (IBMS10011)
|School||Deanery of Biomedical Sciences
||College||College of Medicine and Veterinary Medicine
|Credit level (Normal year taken)||SCQF Level 10 (Year 4 Undergraduate)
||Availability||Not available to visiting students
|Summary||The course provides students an overview of topics for understanding cognition and intelligence ¿ whether natural or artificial. It discusses how human cognition originates from brain mechanisms and how its principles inspire and inform artificial intelligence (AI) approaches. The course discusses advances and methodologies of cognitive neuroscience and artificial intelligence in an integrated way. It is organized around 4 topics (learning, decision making, knowledge representations and social intelligence) which are addressed from both perspectives. We aim for students to develop a good understanding of each discipline, its basic techniques, similarities and differences between cognitive neuroscience and AI, how their advances can impact health and society, and the importance of ethical considerations.
Brain, Cognition and Artificial Intelligence 4 covers topics in cognition and intelligence ¿ both natural and artificial. It discusses how human cognition originates from brain mechanisms and how its principles inspire and inform AI approaches. The course is organized around 4 topics (learning, decision making, knowledge representations and social intelligence) which are addressed from neuroscience and AI perspectives. Through tutorials, practicals, discussions and debates we aim for students to develop a good understanding of each discipline, their basic techniques, similarities and differences as well as impacts on health and society. Group project will provide a valuable group work experience addressing a real-life problem. Debates will help facilitate public speaking skills and ability to productively engage in a professional discussion addressing both specific topics and their broader impacts.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Two ICAs in a closed-book exam format to test knowledge and understanding (individual, 25% each).«br /»
A group project tackling a real-life problem using techniques learned in the course (based on a written group report, 25%, with minor individual mark components).«br /»
Participation in live debate and online discussion, moderated by the course team, to discuss recent advances and their social impacts (individual, 25%).
||Students will receive summative feedback on their quizzes, debate/discussion performance and group project report. Additional formative feedback will be given throughout the course, especially in tutorials, discussions and practical sessions, so students can check their understanding as they progress, as well as to practice debate. This includes opportunities for formative peer feedback. Students will also have to add a reflective element to their group project report.
|No Exam Information
On completion of this course, the student will be able to:
- Critically discuss how various aspects of cognition and intelligence arise from brain mechanisms.
- Critically discuss fundamentals of artificial intelligence (including machine learning).
- Compare and contrast neural mechanisms of cognition and intelligence with those used in AI applications.
- Apply their knowledge to solve a real-life problem.
- Critically discuss health, social and ethical implications of advances in cognitive neuroscience and AI.
|Graduate Attributes and Skills
||Cognitive, social and computational neuroscience:
Ability to understand how brain systems produce higher order functions and how these can be analyzed and modelled ¿ individually or in a social setting; knowledge of neuroimaging approaches, model-based analysis of brain and behavior, key behavioral and social economics theories.
Ability to use a number of machine learning, planning & problem solving, natural language processing, neuroinformatics, and human computer interaction techniques; knowledge of differences and similarities with neuroscience approaches; ability to distinguish computational neuroscience from neural computation.
Time management, project management, independence, ability to synthesize information, ability to complete projects, critical thinking, analytical reasoning, scientific creativity, collaborative ability, ability to communicate scientific concepts and their broader implications to an audience; ability to justify particular points of view in a professional and effective matter.
|Keywords||Cognitive neuroscience,social neuroscience,computational neuroscience,neuroeconomics,artificial
|Course organiser||Dr Gediminas Luksys
Tel: (0131 6)50 3525
|Course secretary||Miss Natasha Goldie