BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20260927T111210EDT-2507Lv5lkD@132.216.98.100
DTSTAMP:20260927T151210Z
DESCRIPTION:Using Machine Learning Methods to Predict Tuberculosis Treatmen
 t Resistance.\n\n\nMachine-learning algorithms are used to detect complex\
 , often unforeseen patterns within rich datasets. There are two general ca
 tegories of algorithms: unsupervised and supervised. Supervised machine-le
 arning algorithms\, the topic of this presentation\, start out with a hypo
 thesis and categories that are set out in advance. These algorithms are th
 en “trained” on data for which the outcomes of interest are known\, with t
 he training process continuing until a desired level of accuracy is achiev
 ed. These results are then used to make predictions based on out-of-sample
  data for which the outcome of interest is not known. While most statistic
 al models can be viewed as a simpler form of machine-learning algorithm th
 at imposes a pre-determined functional form for the relationship between t
 he predictors and the outcome of interest\, more advanced machine-learning
  algorithms impose much less structure and can therefore detect very compl
 ex and intricate relationships in high-dimensional data (i.e.\, data with 
 several different types of variables\, possibly including quantitative\, t
 ext and image information). Advances are now being made in analyzing the o
 utput of these algorithms to permit assessment of the relative importance 
 of each variable. The current talk will provide an introduction to neural 
 networks\, an advanced supervised machine learning method. The methodology
  is then applied to lab data from the World Health Organization (WHO) to i
 dentify gene mutations associated with resistance to tuberculosis treatmen
 t that are amenable to targeted drug therapy.\n
DTSTART:20161025T193000Z
DTEND:20161025T203000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Jimmy Royer\, Analysis Group
URL:https://www.mcgill.ca/mathstat/channels/event/jimmy-royer-analysis-grou
 p-263579
END:VEVENT
END:VCALENDAR