Fall 2016 Computer Science 458: 12/07/2016

Administrivia

Final Project: Criteria

• Originality. I appreciate the degree to which you come up with new ideas. There are lots of ways to tackling the projects.

• Functionality. The program should work. It is best if we can run it ourselves, such as on the zoo. At the very least, you should provide ample evidence that the program does what it is supposed to do.

• Explanation. The program should explain its behavior. A good way to think about this is how you would explain your program's output to someone observing your program in action.

• Relevance to course. The program should have a connection to a topic or topics discussed in class, including the guest speakers. The more direct the connection, the better. Note that the best connection is through a method or technique, not a domain. Thus, applying Monte Carlo simulation to medicine is better than applying astrology to finance.

• Clarity. This is valued in thought, word, and deed. How clear is your concept? Do you describe it well in your write-up? Does your program clearly reflect your idea?

• Success. Actually, not so much. In a freshman chemistry course, I selected a project of making polywater - a polymer form of water which had been reported by Soviet scientists. The professor encouraged me to pursue this and it failed. I was unable to replicate the Soviet experiment. (As it turns out, neither was anyone else.) My point is that you may learn from failure as well as from success.

Lecture

Final Project: The Hidden Agenda

Soon, you are going into the real world, or graduate school. You will be expected to identify and tackle problems that no one has solved before. This course was meant to provide a bridge to that world - to acclimatize you to various domains and provide you with multiple ways of thinking about decision making. In addition, the languages R and Python are valued outside academia.

Domains	Methods
Games Finance Risk management Investing Self-driving cars … Sports Recommender systems Medicine Movies	Economic decision theory Monte Carlo simulation Capital budgeting Net Present value Rule based systems Case based systems Goal based systems … Machine learning Statistical models - linear regression

My emphasis on cognitive science for decision making reflects the need to maintain a focus on the human in the loop, and particularly the importance to explain decisions.

The problem sets were often open ended, which required you to be resourceful. The guest speakers reinforced the idea that there is a spectrum of techniques that can be applied across a range of problem domains. The real world requires that you be open minded, and willing to learn new domains and new methodologies.

This course was meant to provide an intellectually safe space for you to experience those challenges. That way, you won’t be surprised when you see it in the real world.

Each of you had to weigh the resources you had and define what a solution looked like. With the project, you have the opportunity to identify a need or a problem and to come up with an original solution.

I am pleased to see the energy and ambition of the class as reflected in the project proposals. The main point is that I want to encouraged you to think independently. That is why I give you a final project instead of a final exam.