CIS 520: Machine Learning
COURSE DESCRIPTION:
CIS 520 provides a fundamental introduction to the
mathematics and practice of machine
learning. Probabilistic and statistical methods for prediction
and clustering are covered in depth. Topics covered include linear and logistic
regression, feature selection, support vector machines, EM, k-means,
graphical models, dimensionality reduction (PCA, CCA, LDA, ...), and deep learning.
For details, see the course wiki which may only be readble from the upenn.edu domain (sorry).
AUDIENCE:
The course is aimed broadly at advanced undergraduates
and beginning graduate students in computer science, electrical engineering,
mathematics, physics, and statistics. Undergraduates who meet the prerequisites are particularly encouraged to enroll, as are students from other departments.
PREREQUISITES:
- Multivariable calculus
- Linear algebra
- Elementary probability
- Programming experience in a language such as C, Java, or Matlab