We shall often use ipython notebooks to deepen our understanding for concepts of the lecture course. Please just get acquainted with Jupyter notebooks. In the sequel relevant material for the lecture as well as notebooks are linked.
You can find two great notebooks at Peter Norvig's inspiring page illustrating simple and advanced concepts of basic probability. A modified notebook containing addtionally examples on random walks and Monte Carlo emethods can be found at in raw form or in html form.
A notebook containing a Monte Carlo estimation of pi by means of a self-coded random number generator can be found at in raw form or in html form.
A notebook containing material on dynamic Bayes updating in case of independent Bernoulli trials with unknown success rate or in case of independent Gaussian ranom numbers with unknown mean can be found at in raw form or in html form.
A notebook containing an example on maximum likelihood estimation can be found at in raw form or in html form.