In the fall 2015, the course will focus on machine learning.
The field of machine learning is concerned with the automatic discovery of
regularities in data through the use of computer algorithms and with the use
of these regularities to take actions such as classifying the data into
different categories. In this course, we mostly consider the following
sub-fileds of the machine learning:
(1) Supervised learning, where we have access to a known training data
set. The training data comprises examples of the input vectors along with
their corresponding target vectors. The goal would be to train a model
using the training data in order to use the model for a test data set
later. The classification and regression problems are two known examples
of the supervised learning.
(2) Unsupervised learning, where we do not have access to a known training
data set. The goal in such unsupervised learning problems may be to
discover groups of similar examples within the data, where it is called
clustering, or to determine the distribution of data within the input
space, known as density estimation.
Here in this course we cover these two areas of learning in details. As an
example we will speak about linear regressions, PAC learning model, EM and
clustering, kernels, dimensionality reduction techniques like PCA and SVD
and learning mixture of distributions among the others. We also discuss
the new advances of these areas with respect to big data models such as
streaming and MapReduce models. This includes new sketching and sampling
techniques that have been developed very recently for supervised and
unsupervised learnings when the data is big.
Poslední úprava: IUUK (04.05.2015)
