AI News, Artificial Intelligence for Computational Sustainability: A Lab Companion/Machine Learning for Prediction/Unsupervised Learning and Ecological Footprints

Artificial Intelligence for Computational Sustainability: A Lab Companion/Machine Learning for Prediction/Unsupervised Learning and Ecological Footprints

This in-class exercise was done towards the end of an introductory course on Artificial Intelligence, as part of a regular computer science and engineering program ABET accreditation on the criterion concerned with lifelong learning.

In addition students were given a hardcopy of the Ross, et al paper, and told that they could use this during the exercise, and that they would be turning in their marked up hardcopy of the paper with their answers to the exercise, along with any notes that they prepared before walking into class.

It’s ok to cut and paste and adapt text from the Ross et all paper – just quote text from that paper that you use if that is what you do (but significant paraphrasing requires no further acknowledgement in this context – your use of the ideas in the Ross et al paper in this exercise is taken as a given).

Instructor comments: A fully correct answer would be ‘A better carbon calculator groups users into clusters based on similar responses to geographical and carbon-lifestyle questions, to better predict unknown choices be subsequent users (or expanding this latter point -- a user who does not answer all questions can be placed in the best-fitting cluster based on known responses, with cluster average values predicted as the user’s unknown responses).

This question is intended to assess student ability to understand and express the gist of the relationship between an application (carbon calculator) and a process abstraction (clustering) that can implement the application.

Ideally, an answer made unambiguous reference to (i) known variables that represented the basis of ascertaining cluster membership, and (ii) cluster membership as a basis of predicting values for unobserved variables (unknown user responses).

Instructor comments: (e) and (f) are intended to assess the ability of students to critically assess material that they read, and to highlight that uncertainty in their understanding of this material is an invitation to follow up.

(e) and (f) are counted together, with credit given for points raised in (f) and/or (g) (using a discounted scale of 1.5, 1.0, 0.5, 0.5) that seemed a legitimate issue to the instructor.

(i) inadequate description of experimental design, (ii) variance measures not reported for experimental results, (iii) implementation details of the BCC (it is the lack of great detail which makes the paper ideal in some ways for this assessment, allowing students to imagine a possible implementation), but other points are possible.

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