This course is intended as an introduction to solving problems by coding solutions in the MATLAB programming environment. It assumes no prior knowledge of programming or coding skills. Students will develop a beginner’s skill level for deriving algorithms. This will be complemented by them learning how to use the MATLAB language and integrated development environment in concert to code these algorithms as functions. The development of the students’ skills and knowledge base will be done in the context of them encoding data; processing the data with respect to a given problem; and outputting a correct answer in the appropriate format.  

The course begins with an introduction to the concepts of data encoding and the methodology of writing functions. There is also a good deal of time spent on getting the students familiar with the programming and evaluation environments. From that foundation, students are exposed to variables, functions, and scope. The course then expands the students’ abilities to deal with data collections of vectors and arrays. Next, they learn the power of conditional and iteration statements. They then use these abilities to deal with the more complex data collections of cell arrays, spreadsheets, text files, structures, and directory information. The course also provides instruction on how to make plots of the results of their data analyses.  It concludes by exposing students to images.

In this course, you will learn how to program all the major systems of a robotic car based on lectures
from the former leader of Google’s and Stanford's autonomous driving teams, Sebastian Thrun. You
will learn some of the basic techniques in artificial intelligence, including probabilistic inference,
planning and search algorithms, localization, tracking, and PID control, all with a focus on robotics.
Extensive programming examples and assignments in Python will apply these methods in the context
of autonomous vehicles.