Welcome to our introductory class on Machine Learning!
Course timetable:
Monday 14:30-16:00, P150 (Complesso Paolotti)
Wednesday 14:30-16:00, P150 (Complesso Paolotti)
All classes will be also on live streaming (Zoom) and the videos will be posted on Moodle.
Course material:
There are no required textbooks for this course. Slides and other material will be posted periodically on the class Moodle. However, you can use as (non-mandatory) additional material
T. Mitchell, "Machine Learning", McGraw Hill, 1998 (warmly suggested)
E. Alpaydin, "Introduction to Machine Learning", Cambridge University Press, 2010
C.M. Bishop, "Pattern Recognition e Machine Learning" Springer, 2006
Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning” MIT Press, 2016
Prerequisites:
Students are expected to have the following background:
Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program in Python/numpy
Familiarity with probability theory and linear algebra