Question: I am an assistant professor preparing a 5 lecture series 'introduction to programming'. The target audience are first year engineering undergrads - from a mix of mechanical, electrical, electronic, telecomms and civil engineering majors. Some of these students will never have programmed before. We will use Matlab.

I am expecting that many of these students will never have coded before and almost all will not have seen Matlab before. They will need Matlab in all subsequent years of their degree.

Given the extremely limited time for this course, how can I give best bang for their buck, without it being too dry?

Is there something cool we could work towards creating over the 5 weeks?

More info: There are 5 x 4 hours sessions. Classroom cannot be flipped as there will be 50+ students.

Editing here to thank everyone who has taken the time and effort to give extremely helpful detailed replies. I appreciate it guys.

Answer: 
With only 5 sessions, I would abandon the idea of building something cool, and focus with laser-like precision on whatever they *need to know* going forward in their coursework. There is such a small amount of time to learn the basics of any programming language.

MatLab supports lambdas, objects, and GUI design, but unless they are going to get into rather serious programming, I imagine that you would steer clear of these. (I don't know how you would cover any of them in 5 days with novices in any case.)

The danger in heading towards UI and a game is that they will come out without being able to do what they presumably need in the next class, such as matrix multiplication, or graphing an integral multiplication. You may be better using your five sessions to delve into five areas of greatest need, as determined by the future required coursework of your students.
I studied and TA'd electrical engineering and computer science in college and graduate school. You have a unique opportunity versus most introductory programming languages that use general languages like java or python because Matlab, which is a portmanteau for "Matrix Laboratory", is a language that is meant to be very interactive, intuitive to mathematically-savvy students, and easy to pick up and includes the constructs of 1-d vectors, 2-d matrices, and n-dimensional tensors as first-order data structures (I know you know all this, but I'm stating it for other readers who may be less familiar with this language). 

Most of your students will have a background in linear algebra and matrix notation and use in mathematics from their courses in all of the state engineering disciplines. Therefore, you can focus less on teaching fundamental of data types, data structure, control flow logic, hierarchy and namespaces, and instead build up from intuitive Matrix-like mathematical operations that naturally have sequences of repeating steps with exit conditions.

* 1st-week of introduction to typical matrix problems in the engineering sciences, and walking them through building the simple scripts to execute those.
* 2nd-week add Matlab function wrapper per file concept as an introduction to hierarchy and namespace (informally, this is how it works, not focusing on the deeper theoretical aspects) of scoping local variables in input/return values.
* 3rd-week show how there are lots of the simple operations (like matrix multiples, inverting matrices, DFT and FFT, that are prebuilt library functions, and study some tradeoffs on choosing them and how they deal with padding, non-invertible data sets, etc.)
* 4th and 5th-week focus on a large programming project that does something interesting with larger data and doing something iterative like gradient descent on a simple pattern match like regression, or simple cases of convex cost function search, or even backpropagation in a 3-layer neural network. 

Pre-build much of the base and helper code and have them learn how to use what they learned so far to code the inner loop of the iterations. Definitely give them the code that they can easily tweek so they get some experience generating graphics to demonstrate (and debug) the optimization iteration sequence.