Jan 7: Course overview, intro to scientific Python

Today was the first lecture and we discussed course organization and took a brief look at using Python for Machine Learning.

Things to remember from today:

• Passing the course requires: 1) exercises (at least 60%), 2) assignment (competition; TBA) and final exam.
• Register for the exercises in POP.
• Remember to register a group for the competition (max 4 members). The deadline is 14.1.
• You can use classroom (TC303) computers or your own laptop in the exercise sessions. If you use your own, we recommend to install anaconda python (or miniconda with appropriate packages).

The lecture slides are available at the course website.

The first hour concentrated mostly on the organization of the course. On the second hour, we looked at the beginning of the first slide set. First we emphasized the difference between model based and training based approach for solving recognition and detection problems.  

• If, for example, the problem is to detect whether a sinusoidal beep is present in an audio signal, there is no point to solve it by showing examples. This is because there is a perfect model (formula) for the sinusoid, and we can mathematically define exactly what we are looking for.
• On the other hand, if the task is to classify pictures of cats and dogs apart, the model based approach is no longer useful: there is no formula that would describe all possible pictures of cats or dogs.