cs 584 - Machine Learning
Computers are increasingly more powerful
and so enable us to solve increasingly difficult problems.
This ability opens a path to a multitude of new applications.
Machine learning provides us with tools to address complex
problems. For example, we can use computers to make
predictions on future behavior (e.g. predict the values of
stocks or predict dangerous traffic conditions while driving),
diagnose conditions (e.g. diagnose a disease in humans, detect
abnormal conditions in a computers, or detect signs for credit
fraud), estimate unknown and constantly changing model
parameters (in a vast range of applications in virtually all
areas of computer science), explore large amounts of data in
search for critical information (e.g. explore legal data, or
analyze patterns in social networks), recognize objects in
images (e.g. identify faces and fingerprints, recognize
handwritten or machine printed text), understand video
sequences (e.g. rigid motion of cars, or articulated human
motion), understand audio signals (e.g. voice recognition),
interact with humans through more natural means (e.g. identify
human emotion and understand implicit/explicit human intention
by observation), control autonomous robots (e.g. avoid
obstacles or control articulated motion). The possibilities
are endless.
The examples above illustrate some difficult problems to which
we often do not have simple models or direct solutions.
Moreover, partial models often result in observations and
behavior that may appear random. Machine learning in concerned
with the solution of such difficult problems. Instead of
employing the usual approach of specifying the model
explicitly (e.g. by programming a known sequence operations),
machine learning employs algorithms that learn the models
directly from the data. Learning models from data allow us to
address complex problems such as the ones above. As such,
machine learning algorithms are important in virtually almost
all areas of computer science. Topics to be covered by cs584
in this semester include: overview of machine learning
techniques, parametric regression, supervised learning, neural
networks, support vector machines, computational learning
theory, unsupervised learning, dimensionality reduction,
graphical and sequential models. The course assumes some
programming experience, and a basic knowledge of calculus,
statistics, and linear algebra. For further details please
refer to the course website or contact the course instructor.
