Teaching Machine Learning: Student Project Reports for CPSC 599.66 and 601.66 Winter 2007

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dc.contributor.authorRichter, Michaeleng
dc.contributor.authorBilawshuk, Tyler
dc.contributor.authorLeclerc, Eric
dc.contributor.authorMcClocklin, Landon
dc.contributor.authorLyons, Allan
dc.contributor.authorKendon, Tyler
dc.contributor.authorKidney, Jordan
dc.contributor.authorXu, Hong
dc.contributor.authorMacKas, Brenan
dc.contributor.authorObied, Ahmed
dc.contributor.authorOlsen, Luke
dc.contributor.authorPark, Justin
dc.contributor.authorWalker, Scott
dc.contributor.authorOlsen, Luke
dc.contributor.authorPark, Justin
dc.contributor.authorTkachyk, Stephanie
dc.contributor.authorMa, Lizhe
dc.contributor.authorKianmehr, Kevin
dc.date.accessioned2008-02-26T22:41:37Z
dc.date.available2008-02-26T22:41:37Z
dc.date.computerscience2007-04-25eng
dc.date.issued2007-04-25eng
dc.description.abstractTeaching machine learning has two parts. One part is the lectures. These can be found under www.cpsc.ucalgary.ca/~mrichtet/ml. But lecturing is only half of the story. That is, because passive learning by listening does not provide the same expertise compared to active learning by doing. For this purpose a project work was required. Students had the choice to work on their own or to form a group of two. At the beginning of the course, after some introduction and overview, the projects started. The start had the following steps: 1) Selecting a domain of application as, e.g. spam filters, playing games, cooperative multiagents etc. 2) Formulating a learning goal in that domain, as improving cooperation. The choice was completely free. 3) Selecting one or more candidates for learning techniques presented in the course that were focused in the sequel. These topics were presented first very early and then in some more detail at midterm. In this volume the final reports are listed. Particular emphasis was put on the aspects of the difficulties that occurred during the project and how to overcome them. The difficulties had different sources. The major ones are problems with the tools and getting enough data, or underestimating the complexity. The free choice of the application domain had the consequence that the authors were quite familiar with it, could use existing environments and use the results for further activities like masters or PhD theses. Formal projects implementation details are available, write to mrichter@cpsc.ucalgary.caeng
dc.description.notesWe are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.caeng
dc.identifier.department2007-859-11eng
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/30969
dc.identifier.urihttp://hdl.handle.net/1880/45611
dc.language.isoEngeng
dc.publisher.corporateUniversity of Calgaryeng
dc.publisher.facultyScienceeng
dc.subjectComputer Scienceeng
dc.titleTeaching Machine Learning: Student Project Reports for CPSC 599.66 and 601.66 Winter 2007eng
dc.typeReport
thesis.degree.disciplineComputer Scienceeng
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