PPT-CS 478 - Ensembles

1 Ensembles CS 478 Ensembles 2 A Holy Grail of Machine Learning Automated Learner Just a Data Set or just an explanation of the problem Hypothesis Input Features Outputs CS 478 Ensembles. PhilipBachmanMcGillUniversityMontreal,QC,Canadaphil.bachman@gmail.comOuaisAlsharifMcGillUniversityMontreal,QC,Canadaouais.alsharif@gmail.comDoinaPrecupMcGillUniversityMontreal,QC,Canadadprecup@cs.mcgi 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. & . Local Environmental Knowledge. Ken . Salo. . DURP at UIUC. kensalo@illinois.edu. . Personal Introduction. B. Science and Masters in Public Law (LLM);. At . Cape Peninsula University of . Technology (CPUT) I . Latest Results on outlier ensembles available at http://www.charuaggarwal.net/theory.pdf (Clickable Link) tsub-topics(eg.bagging,boosting,etc.)intheensembleanalysisareaareverywellformalized.Thisisrem 1. Statistical Significance and Performance Measures. Just a brief review of confidence intervals since you had these in Stats – Assume you've seen . t. -tests, etc.. Confidence Intervals. Central Limit Theorem. Which of the two options increases your chances of having a good grade on the exam? . Solving the test individually. Solving the test in groups. Why?. Ensemble Learning. Weak classifier A. Ensemble Learning. MUS 863. The Auditioned Ensemble. PROs. Option of creating a balanced ensemble. Separates groups by ability . Auditioned Ensemble. CONS. Separation by ability could create an unwanted . hierarchy. Students attribute success to musical ability, and not effort. from Finite Correlation Length . Fernando . G.S.L. . Brand. ão. Microsoft Research. Quantum Spin Systems, Recent Advances, . Cergy. -. Pontoise. , 2015. based on joint work with . Marcus Cramer . University of Ulm. 1. Evolutionary Algorithms. CS 478 - Evolutionary Algorithms. 2. Evolutionary Computation/Algorithms. Genetic Algorithms. Simulate “natural” evolution of structures via selection and reproduction, based on performance (fitness). 1. Learning Sets of Rules. CS 478 - Learning Rules. 2. Learning Rules. If (Color = Red) and (Shape = round) then Class is A. If (Color = Blue) and (Size = large) then Class is B. If (Shape = square) then Class is A. Safety Training. purpose. Understand the appropriate safety measures and who to contact in an event of an emergency. Aid in the safety of students in your designated building. Become familiar with the evacuation and assembly areas both inside and outside of your building. Lucy . Kuncheva. School of Computer Science. Bangor University. mas00a@bangor.ac.uk. . Part 1. 1. What is Pattern Recognition? . Data set: objects, features, class labels. Classifiers and classifier ensembles. Week 2. Review from Week 1. Elements of Music. Tempo. Review from Week 1. Elements of Music. Dynamics. Review from Week 1. Elements of Music. Texture. Review from Week 1. Review from Week 1. Active Listening. purpose. Understand the appropriate safety measures and who to contact in an event of an emergency. Aid in the safety of students in your designated building. Become familiar with the evacuation and assembly areas both inside and outside of your building.