diff --git a/projects/Makefile b/projects/Makefile
new file mode 100644
index 0000000..986268d
--- /dev/null
+++ b/projects/Makefile
@@ -0,0 +1,16 @@
+all:
+ for d in `ls -d project_*/`; do \
+ echo "Processing $$d" ; \
+ cd $$d; $(MAKE) zip ; \
+ done
+
+ mv project_*/*zip .
+
+clean:
+ for d in `ls -d project_*/`; do \
+ echo "Cleaning up $$d" ; \
+ cd $$d; $(MAKE) clean ; \
+ done
+
+ rm -f *.zip
+ rm -rf auto
diff --git a/projects/disclaimer.tex b/projects/disclaimer.tex
new file mode 100644
index 0000000..11e7bab
--- /dev/null
+++ b/projects/disclaimer.tex
@@ -0,0 +1,15 @@
+ \fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
+ in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
+ submit in ILIAS. If the assignments include programming
+ exercises, hand in a pdf for the questions, the .py files for
+ the programs, and the data in one zip file.
+
+ Use complete and correct sentences unless otherwise
+ noted. Please be succinct. Use your own words. Write down a
+ concise reasoning, not just the result. We expect you to do
+ exercises on your own, but you are encouraged to discuss the
+ exercises with your fellow students. If you blindly copy your
+ results from others, you miss out on a chance to learn something
+ new. Use all resources available to you, but always make sure
+ that you truly understand why you give the answer you give.
+ }}
\ No newline at end of file
diff --git a/projects/project_PCA_natural_images/Makefile b/projects/project_PCA_natural_images/Makefile
new file mode 100644
index 0000000..b7abffb
--- /dev/null
+++ b/projects/project_PCA_natural_images/Makefile
@@ -0,0 +1,10 @@
+latex:
+ pdflatex *.tex
+ pdflatex *.tex
+
+clean:
+ rm -f *.log *.aux *.zip *.out
+ rm -f `basename *.tex .tex`.pdf
+
+zip: latex
+ zip `basename *.tex .tex`.zip *.pdf *.dat *.mat
diff --git a/projects/project_PCA_natural_images/pca_natural_images.tex b/projects/project_PCA_natural_images/pca_natural_images.tex
new file mode 100755
index 0000000..25f903c
--- /dev/null
+++ b/projects/project_PCA_natural_images/pca_natural_images.tex
@@ -0,0 +1,217 @@
+\documentclass[addpoints,10pt]{exam}
+\usepackage{url}
+\usepackage{color}
+\usepackage{hyperref}
+
+\pagestyle{headandfoot}
+\runningheadrule
+\firstpageheadrule
+
+\firstpageheader{Essential Statistics}{Homework 01 due 10/29/2014 23:59 am}{23. October 2014}
+\runningheader{Homework 01}{Page \thepage\ of \numpages}{23. October 2014}
+\firstpagefooter{}{}{}
+\runningfooter{}{}{}
+\pointsinmargin
+\bracketedpoints
+
+%\printanswers
+\shadedsolutions
+
+
+\begin{document}
+%%%%%%%%%%%%%%%%%%%%% Submission instructions %%%%%%%%%%%%%%%%%%%%%%%%%
+\sffamily
+\begin{flushright}
+\gradetable[h][questions]
+\end{flushright}
+
+\begin{center}
+ \fbox{\parbox{0.985\linewidth}{ \small Please answer all questions
+ in an electronic file (.txt, .doc are ok, but we prefer .pdf) and
+ submit in ILIAS.
+
+ Use complete and correct sentences unless otherwise
+ noted. Please be succinct. Use your own words. Write down a
+ concise reasoning, not just the result. We expect you to do
+ exercises on your own, but you are encouraged to discuss the
+ exercises with your fellow students. If you blindly copy your
+ results from others, you miss out on a chance to learn something
+ new. Use all resources available to you, but always make sure
+ that you truly understand why you give the answer you give.
+ }}
+\end{center}
+
+%%%%%%%%%%%%%% Questions %%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{questions}
+ \question {\bf Reading assignment: Do not submit answers to this
+ question! }
+
+ Read chapter 1. up to 2.4 (including) of Samuels/Wittmer/Schaffner.
+
+ Pay special attention to the following questions.
+ \begin{enumerate}
+ \item What types of scientific evidence do the authors list? How
+ strong are these evidences?
+ \item What are the different types of data encountered in
+ statistical analysis?
+ \item What is a population? What is a random sample? What are
+ sampling errors? What are nonsampling errors?
+ \item What is a descriptive statistic?
+ \item What property do robust statistics have?
+ \end{enumerate}
+
+ \question Install python and a suitable editor on your computer.
+ \begin{parts}
+ \part For installing python, I recommend the anaconda
+ distribution: \url{http://continuum.io/downloads}. It does not
+ matter whether you install python 2.7 or 3.4. I will use python
+ 3.4 syntax.
+
+ \part As editor I recommend either sublime text (for people new to
+ programming) or pycharm (for people with programming
+ experience). I do not recommend to use a text editor that comes
+ with your operating system (like word pad). Text processing
+ programs like Mircosoft Word or Libre-Office {\bf won't work at
+ all}. Programming needs a little more than just typing text and
+ you will make your life unnecessarily hard by using an editor not
+ suited for it.
+ \part Find out how to run a python program on your operating
+ system and how to install new python packages. Install the
+ packages {\tt pandas} and {\tt seaborn}.
+ \end{parts}
+
+ \question To publish scientific results, you will usually need
+ to use statistical methods. Some journals provide you with a brief
+ description of how they expect you to apply statistical methods. One
+ example can be found in the author guidelines of the journal
+ Nature
+
+ \begin{center}
+ \url{http://www.nature.com/neuro/pdf/sm_checklist.pdf}
+ \end{center}
+
+ Please read the ‘checklist’ and answer the following questions:
+
+ \begin{parts}
+ \part[2] Why is it important that statistical methods are applied
+ correctly?
+
+ \begin{solution}
+ When not applied correctly, the results of statistical methods
+ might not support your hypothesis and can lead to false
+ conclusions.
+ \end{solution}
+
+ \part[2] Name two common descriptive statistics and what you have
+ to specify for them in nature.
+
+ \begin{solution}
+ \begin{itemize}
+ \item A clearly defined number $n$ of data points should be
+ specified. If the sample is small, plot points instead of
+ using descriptive statistics. Errorbars should be clearly
+ defined.
+ \item measure of center: mean, median
+ \item measure of variability: standard deviation, range
+ \end{itemize}
+ \end{solution}
+
+ \part[3] Name one statistical test that you have heard of or
+ used. If you were to apply any of them, what would you have to
+ specify to follow the Nature guidelines?
+
+ \begin{solution}
+ {\bf Student's T-Test} for testing whether the mean of two
+ populations is the same
+ \begin{itemize}
+ \item a clearly defined $n$ for the test
+ \item a justification for the sample size used
+ \item a clear description of the statistical method: since the
+ t-test is very common, stating that a two independent sample
+ t-test was used should be sufficient.
+ \item Justify that the data meets the definition: the two
+ populations should be normally distributed with the same
+ variance; the data was sampled independently from the two
+ populations being compared.
+ \item Is the variance in the different groups different.
+ \item was it one-sided or two-sided
+ \end{itemize}
+ \end{solution}
+
+ \part[3] Why are you asked to justify each incidence in which
+ you exclude some of the data that you collected? What could be a
+ valid reason to exclude a data point?
+
+ \begin{solution}
+ Excluded data points might make a sample from a population not
+ representative anymore, and can therefore alter the outcome and
+ conclusions of a study. They might be excluded if there is a
+ good reason to believe that they are not part of the population
+ under investigation.
+ \end{solution}
+
+ \end{parts}
+
+ \question {\bf Robust statistics} In 1888, P. Topinard published
+ data on the brain weights of hundreds of French men and women. Here
+ are ten brain weights (in Gramm) of female brains from the dataset
+ \begin{center} [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
+ \end{center}
+
+ Open a new file ``brain\_weight.py'' with you text editor to write
+ the following python program (please hand in the plots and the program).
+ \begin{parts}
+ \part[2] Create a list that contains the above brain weights.
+ \part[2] Create a new list that contains the following ten means:
+ Each mean is computed from the original list after removing one
+ element (hint use slicing and adding lists for that; we did this
+ in the lecture already). {\bf Warning:} I {\em do not} expect you
+ to use {\tt for}-loops. Only use them if you know them already. If
+ you do use them, be prepared to explain your code to me to get
+ credits for this task.
+ \part[2] Create yet another list that does the same, only for the
+ median.
+ \part[2] Make a boxplot with the different means and medias (like
+ in the lecture). To show the plot at the end of the program
+ you need to put a {\tt plt.show()} at the end of the program. If
+ you want to save the plot, put the command {\tt
+ plt.gcf().savefig('YOUR\_NAME\_homework01.pdf')} before that. Label
+ the y-axis by using the function {\tt plt.ylabel('FILL IN YOUR LABEL')}
+ \part[2] What can you observe and what does that tell you about
+ the robustness of the statistic?
+ \end{parts}
+ \begin{solution}
+ \begin{verbatim}
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+sns.set_context("paper", font_scale=1.5, rc={"lines.linewidth": 2.5})
+
+w = [1125, 1027, 1112, 983, 1090, 1247, 1045, 983, 972, 1045]
+
+brain_means = [ np.mean(w[1:]), np.mean(w[:1] + w[2:]), np.mean(w[:2] + w[3:]), \
+ np.mean(w[:3] + w[4:]), np.mean(w[:4] + w[5:]), np.mean(w[:5] + w[6:]), \
+ np.mean(w[:6] + w[7:]), np.mean(w[:7] + w[8:]), np.mean(w[:8] + w[9:]),\
+ np.mean(w[:9]) ]
+brain_medians = [ np.median(w[1:]), np.median(w[:1] + w[2:]), np.median(w[:2] + w[3:]), \
+ np.median(w[:3] + w[4:]), np.median(w[:4] + w[5:]), np.median(w[:5] + w[6:]), \
+ np.median(w[:6] + w[7:]), np.median(w[:7] + w[8:]), np.median(w[:8] + w[9:]),\
+ np.median(w[:9]) ]
+
+sns.boxplot([brain_means, brain_medians], names=['means', 'medians'])
+plt.ylabel('brain weight [g]')
+plt.gcf().savefig('fabian_sinz_homework01.pdf')
+plt.show()
+ \end{verbatim}
+ \end{solution}
+
+
+\end{questions}
+
+
+
+
+
+\end{document}