MAA 540 Machine Intelligence (Spring 2022)

Dale Lehman

Dale.Lehman@loras.edu

AI (Artificial Intelligence) and Machine Learning, often used interchangeably, are the latest buzzword to have usurped Big Data.  The promises, and dangers, are large indeed.  Algorithms have outperformed doctors, teachers, Jeopardy contestants, chess masters, and many other trained professionals.  They may soon take over driving and battlefields.  They have proven to be more efficient and more accurate than humans, and often less biased as well (though their biases exist and may be harder to identify).  The variety of algorithms that can be used to base decisions on data grows daily (mostly some variety of decision trees, neural networks, convolutionalnetworks, boosted trees, random forests, and regression models).  As these modeling techniques grow more powerful, a new class of software tools has developed - referred to as Auto ML (automated machine learning).  For better or (and) worse, analysis increasingly relies on computer processing of data, and the role of the data scientist/analyst relies on learning to effectively work with computers and humans each doing what they are best at.  This course will explore these tools, their requirements, capabilities, and how they can be deployed in organizational settings.  The cousrse will utilize JMP, Alteryx, KNIME, and DataRobot.

Grading:  There will be homework assignments and a group project.  Homework will comprise 2/3 of the course grade and the group project the other 1/3.  Homework can be resubmitted (all or partially) for up to half of the orginal grade, after it was due and has been gone over in class.  The grading rubric is:

A:  demonstrate a command of the tools

B:  control of the tools, but too many omissions or errors to call it command.

C:  effort is clear, but the tools are controlling you rather than vice versa.

D:  effort is questionable.

F:  lack of effort has been demonstrated.

Textbook:  textbook materials on eLearn.

Brief Outline

Classification Modeling:  data preperation, techniques, validation, model evaluation

Accessing and Using Data

Group Project (TBD)