Archive for November 16, 2018

How Machine Learning Impacts the Undergraduate Computing Curriculum

I’ve been looking forward to seeing this article in print since Ben Shapiro first talked about this, months and months ago. Ben, Rebecca Fiebrink, and Peter Norvig raise the (reasonable) argument that machine learning is now a central activity in computer science, and should be a core topic in undergraduate computing curriculum. What does that mean for what we teach and how we teach it? It’s something that we ought to be talking about.

The growing importance of machine learning creates challenging questions for computing education…

Changes to the Introductory Sequence…These same two aims can also describe introductory courses for an ML-as-core world. We do not envision that ML methods would replace symbolic programming in such courses, but they would provide alternative means for defining and debugging the behaviors of functions within students’ programs. Students will learn early on about two kinds of notional machine—that of the classical logical computer and that of the statistical model. They will learn methods for authoring, testing, and debugging programs for each kind of notional machine, and learn to combine both models within software systems.

We imagine that future introductory courses will include ML through the use of beginner-friendly program editors, libraries, and assignments that encourage students to define some functions using ML, and then to integrate those functions within programs that are authored using more traditional methods. For instance, students might take a game they created in a prior assignment using classical programming, and then use ML techniques to create a gestural interface (for example, using accelerometers from a smartphone, pose information from a webcam, or audio from a microphone) for moving the player’s character up, down, left, and right within that game. Such assignments would engage students in creating or curating training examples, measuring how well their trained models perform, and debugging models by adjusting training data or choices about learning algorithms and features.

 

Source: How Machine Learning Impacts the Undergraduate Computing Curriculum

November 16, 2018 at 7:00 am 5 comments


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