BINF G4008 001 - Intelligent Decision Support: History, Paradigms, Applications
COURSE DESCRIPTION: This seminar-style course will review research in intelligent decision support. The goal of the course is to provide students with foundational knowledge related to design, development, and evaluation of intelligent decision support systems, and prepare them for independent research in this area.
Lena Mamykina, MS, PhD
Wednesdays, 10 am - 12 pm
DBMI Main Conference Room
Competencies: By the end of the course, the students will be able to:
- Identify relevant research paradigms related to design, development, and evaluation of intelligent systems
- Understand domain areas where such systems have been introduced and how successful those systems were
- Summarize existing research based on a set of paper
- Conduct independent literature review
- Identify research gaps in existing research on intelligent decision support
- Formulate research questions to close gaps in existing research
- Conduct literature review and write a report examining existing research related to their research questions
Course Materials: The course material is covered through in-class seminars and discussions. Each seminar will include a set of published papers/book chapters that examine different research paradigms related to intelligent decision support. Assigned readings will be distributed.
Textbooks: There is no textbook, but there will be chapters from different books in the assigned reading list.
Grading: The grading is divided up into three parts.
20% – class participation and discussion: For this seminar-style course, the students will be expected to participate in discussion of assigned readings every week.
20% – leading discussions on selected topics: Each student will be asked to lead the discussion for several class sessions. For this, the students will be asked to carefully examine assigned papers, identify additional relevant papers through independent literature review, identify questions for discussion, and lead the discussion among other students in the class.
30% – midterm: For the midterm, the students will select a research topic within the scope of the course and conduct independent literature review on this topic. The student will write 2-3 page analysis of related work and identify gaps in existing research.
30% – final: For the final, the student will present their proposed research plan to address gaps identified in the midterm.
|#||Topic||Reading Assignment||Report Milestones|
|1||Introduction and course overview||Syllabus, reading list, https://natureecoevocommunity.nature.com/users/24561-richard-buggs/posts/41455-how-to-lead-a-journal-club-meeting|
|2||Decision theory and cognitive foundations of decision making||(1) (chapters 1,2,7), (2), (3)|
|3||Early visions of interactive intelligent systems and AI||Required: (4), (5), (6)|
|4||Expert systems||Required: (8), (9), (10)(chapters 1, 2, and 5)||Identify research questions|
|5||Information visualization and visual analytics||Required: (11), (12),(13),(14)|
|6||Sensemaking and sensemaking support||Required: (16),(17)(18),(19)|
|Identify relevant research areas|
|7||Intelligent tutoring systems||Required: (21),(22),(23)||Midterms due|
|8||Intelligent agents||Required: (24),(25),(26),(27),(28)|
|9||Recommender systems||Required: (32), (33), (34),(35)|
|10||Simulations and predictions||Required: (37), (38)|
|11||Interpretability of intelligent systems||Required: (40), (41)|
|12||Human-Centered AI||Required: (42), (43)|
|13||Clinical decision support||Required: (44–46), (47), (48)||Develop literature search strategy (databases, keywords, etc.)|
|14||Report presentations||Submit final report|
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