"Symphonic metamorphosis of themes by Carl Maria von Weber: A revised wind ensemble transcription of Paul Hindemith's work originally transcribed by Keith Wilson" -- Symphonic metamorphosis of themes by Carl Maria von Weber / Paul Hindemith. / text

Choro for Kristin -- Unlived future -- Poupee -- River of memory. / text

The double up / Lee Morgan -- C.T.A. / Jimmy Heath -- Peace / Horace Silver -- Funk in deep freeze / Hank Mobley -- Escapade / Kenny Dorham -- Nutville / Horace Silver -- Quicksilver / Horace Silver. / text

Oneness within the diversity of music / Christian Kim. / text

Lecture: "Hymns of praise for Debussy: A comparison of the 1920 and 1947 versions of Igor Stravinsky's Symphonies of wind instruments." / text

Lecture: "Kevin Puts' Network: A transcription for wind ensemble." -- Network / Kevin Puts. / text

Empty pockets / Herbie Hancock -- Mercy Street / Peter Gabriel -- Dolphin dance / Hancock -- Actual proof / Hancock -- Tell me a bedtime story / Hancock -- One finger snap / Hancock. / text

Without a song / Vincent Youmans -- Contemplation / McCoy Tyner -- I hear a rhapsody / Fragos -- Lush life / Billy Strayhorn -- I mean you / Thelonius Monk -- Windows / Chick Corea. / text

Adagio para orquesta de instrumentos de viento / Joaquin Rodrigo -- Overture to Candide / Leonard Bernstein -- Visionplace of souls / Kevin Walczyk -- Rocky Point holiday / Ron Nelson -- Passacaglia and Thema fugatum in C minor, BWV 582 / Johann Sebastian Bach -- Give us this day: short symphony for wind ensemble / David Maslanka. / text

This report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents basic decision tree models and advanced ensemble versions. The contribution of this report is to show that basic decision trees perform reasonably well on the gas sensor data, however their accuracy can be drastically improved by employing ensemble decision tree classifiers. The report presents bagged trees, Adaboost trees and Random Forest models in addition to basic entropy and Gini based trees. It is shown that ensemble classifiers achieve a very high degree of accuracy of 99% in classifying gases even when the sensor data is drift ridden. Finally, the report compares the accuracy of all the models developed. / text