Your total score for this course depends on the following:

• Permanent evaluation: Projects Python
• Permanent evaluation: Projects MATLAB
• Permanent evaluation: Defense projects Python
• Permanent evaluation: Defense projects MATLAB

To pass the course, you need to obtain an overall score of at least 50% and a score of at least 50% on the individual Python and MATLAB projects and defenses. You will also have to submit all projects and attend all defenses, if not you will get an "absent" grade.

Weights for the different parts of the course: to be done.

To be done (hopefully not)

All lectures (in PDF format) can be found online (on Blackboard) and at the location announced in class.

In each lab session, you will be given some assignments and/or a project. A typical lab session will take two hours and have the following structure:

• discussion of solution of one or more assignments of the previous lab session (approximately 30 mins);
• demonstration of the new-learned Python constructs in MATLAB (approximately 30 mins);
• solving assignments individually (approximately 45 mins to 1 hour);
• (if applicable) introduction of project (approximately 15 mins)

All assignments are made individually, projects are solved in groups of two students.

These assignments are small exercises to familiarize you with programming concepts. You are expected to finish the assignments before next week's lab session. In the next lab session, one or more of the more challenging exercises will be explained at the beginning of the class, if necessary. Your solutions are not graded.

At the given times in the schedule below, a new "project" is enabled on Blackboard. On every project you will work in pairs of two students.

A project solution will include a program in which you have implemented your solution. Your program will be evaluated as follows:

• it produces the correct results;
• it is well-structured;
• it is well-documented.

For every project, you submit a small report of the project (+- 1000 words, as a PDF). A report typically consists of (and will be graded on) the following:

• a summary of everything you implemented;
• an explanation of how you arrived to your solution (follow the domain/mathematical/programming-phases as described in the course);
• an interpretation and discussion of the simulation results (outcomes, plots, etc, ...).

During the semester, you will be evaluated for every project. At these evaluation moments, you are expected to be able to answer practical (typically using your project solution as context) as well as theoretical questions, as these are your only graded evaluation moments. You will immediately receive feedback.

Your first task will be to form groups of two and subscribe your group on Blackboard. Both students of your group navigate to working tools, then groups to subscribe.

Throughout the course we will use the following tools that are installed in the pc lab. Install them on your personal computer (not required) as follows (using Windows is highly recommended):

• Python 2.7
  • For Windows:
  1. Download and install Python 2.7 (32-bit) or Python 2.7 (64-bit). If you're not sure whether your machine is 32 bits or 64 bits, always choose 32 bits.
  2. Download and install the library Scitools SciTools‑0.9.0.win32‑py2.7.exe (32-bit) or SciTools‑0.9.0.win‑amd64‑py2.7.exe (64-bit).
  3. You will need additional libraries to make Scitools work. To test to make sure all necessary libraries are installed, go through the following steps:
     1. Start Python (command line mode)
     2. In the Python prompt, type the following, followed by pressing enter:
        

        from scitools.std import *

     3. If Python responds with an ImportError, you will need to install the mentioned library (typical libraries include scipy, numpy, matplotlib, ...). You can find the installation file in this list (choose the win32 for 32 bits and win-amd64 for 64 bits, and choose the py2.7 version). When you have installed the library, restart at step 1.
     4. When the above command does not return any ImportErrors anymore, type the following in the prompt, followed by pressing enter:
        

        plot(linspace(0,10,100), sin(linspace(0,10,100)))

     5. If Python responds with an ImportError, go to step 3.
  • For Mac OS X:
  1. Download and install Python version 2.7.
  2. Download and install ActiveTcl 8.6.1.
  3. Download and install Numpy 1.8.0.
  4. Download and install Matplotlib 1.3.1.
  5. Install Scitools:
  1. Download Scitools 0.9.0.
  2. Extract this file.
  3. Open the Terminal and navigate to the location you extracted the file, typically by typing:

     cd ~/Downloads/scitools-0.9.0

     followed by enter.
  4. Type ls followed by enter, and you should see a list of files, including a file called "setup.py".
  5. Type the following to install Scitools:

     python setup.py install

     .
  6. To test the installation, open IDLE and write the following Python code (it should produce a nice sine plot):

     from scitools.std import *
     plot(linspace(0,10,100), sin(linspace(0,10,100)))

• MATLAB
• Download and install MATLAB using these instructions.
• You need Java to be able to use the MATLAB downloader.

Install these tools before the first lab session! If you run into installation problems, contact Bart Meyers.

The burden of timely submission of your work on Blackboard is on you! (late submissions are not accepted)

• Beware that Blackboard's clock may differ slightly from that on your computer. Allow a margin for submission.
• Be sure to check that your submission was actually made. Check for example under Grade Centre whether your (as yet unmarked) submission shows up.