## lectures

This section contains the lectures
given during the course. On the left
are links to all of the lecture
slides.

**Introduction to
R
[2x2]
[source]**
In this lecture we will cover the
programming language R. We will cover
topics such as syntax and semantics,
data structures, and object oriented
programming. In this lecture we will
set up our programming environment and
work through a number of basic
problems which will occurr frequently
in the context of R programming. This
lecture will introduce a majority of
the components necessary to
effectively program in R.

**Introduction to Bioconductor
[2x2] [source]**
In this lecture we will introduce
Bioconductor. This will be necessary
for a number of examples in the future
lectures. This lecture will not be an
exhaustive presentation of
Bioconductor, but rather a short look
at some of the base classes and the
data sets available.

**Exploratory Data Analysis Using R
[2x2] [source]**
In this lecture we will explore the
mechanisms in R for doing exploratory
data analysis. We will take a look at
the graphics systems in R as well as
numerical summary methods. We will
take a look at a couple example data
sets and spend some time on smoothing
methods.

**Statistical Data Analysis Using R
[2x2] [source]**
In this lecture we will explore
hypothesis testing facilities in R as
well as statistical modeling
functions. We will take a look at the
ALL dataset. We will also explore
non-linear least squares and numerical
optimization tools in R.

**Exploring HapMap
Data; Cluster Analysis
[2x2] [source]**
In this lecture we will take a look at
HapMap data. These data have become
ubiquitous in statistical genetics and
represent a commonly encountered data
type in bioinformatics. We will look
at clustering methods as well as some
more interesting methods such as
random forest.

**Hidden Markov Models
and building R packages and Classes
[2x2] [source]**
This lecture will consist of building
an HMM for tiling array data. We will
simulate data, and construct various
fitting and visualization functions to
be used on the real data. In the
process we will get a full example of
using S4 classes to design modular
software and we will finally
incorporate everything into a
package.