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Symbolic Machine Learning (B4M46SMU and BE4M46SMU)


The course will explain methods through which an intelligent agent can learn, that is, improve its behavior from observed data and background knowledge. The learning scenarios will include on-line learning and learning from i.i.d. data (along with the PAC theory of learnability), as well as the active and reinforcement learning scenarios. Symbolic knowledge representations (mainly through logic and graphs) will be used where possible. The lectures are given in English for all students.

Course Organization While Direct Instruction is Suspended due to the Covid Outbreak

Lectures: are given online in this MS Teams channel. The lecture page provides a timetable on the assumption that normal instruction will not resume until the end of semester.

Tutorials: working out exercise problems posted with solutions and homework project assignments. Similarly to lectures, materials posted before the online consultation slots. See the detailed page tutorial page.

Exam: see here.

Additional Information

courses/smu/start.txt · Last modified: 2020/05/10 14:11 by zelezny