Exploring synergies between knowledge representation, reasoning, and machine learning
|General information|| Professor:
, Pratt 398D, sheila-at-cs|
Lectures/Discussions: Thursday 2-4pm in BA2179. *** note *2nd* room change ***
Evaluation: Student evaluation will be based largely on a course project (done in small groups or alone) and on an in-class presentation of a scholarly paper, with a small percentage of marks dedicated to class participation and completion of assigned readings.
Communication: We're using piazza for course communication.
CSC2542 is a seminar-style topics course that explores recent advances in knowledge representation and reasoning. The course draws predominantly on research readings. The format of the course is a mix of class lectures, seminars, and student paper presentations. In Summer 2018, the topic being covered is "Exploring synergies between knowledge representation, reasoning, and machine learning." The area of Artificial Intelligence (AI) Knowledge Representation and Reasoning (KR) employs primarily symbolic methods for representing structured knowledge together with associated reasoning techniques to perform inference. These techniques have been used to address a diversity of problems in AI including question answering, automated planning, diagnosis, program synthesis, verification, and myriad of other problems. The availability of large corpora of data, together with recent advances in machine learning and deep learning have enabled variants of many of these same problems to be explored using distributed and statistical approaches from machine and deep learning (ML). Through research paper readings and a final course project, this course will explore synergies between these two methods of approaching problems in AI.
This is an advanced course, requiring existing knowledge in one or both of KR or ML.
If you're thinking of taking the course but have questions, feel free to contact me.