Master of Music / Department of Music, Theatre, and Dance / Frederick Burrack / Prominent elementary music methods like Orff-Schulwerk, Kodaly, Suzuki, Music Learning Theory, and Dalcroze Eurhythmics share a belief in a sound-before-symbol approach: delaying notation instruction in order to first develop audiation, musical vocabularies, and concrete musical experiences. Unfortunately, piano pedagogy has not taken the same journey, with method books continuing to center on reading from the earliest. While piano pedagogy has made great strides as a professional music community, now it must adopt the same sound-before symbol approach of its general music colleagues, making room for experiential and creative activities as a core component of instruction. Through the integration of movement, exploration, and improvisation activities like the included examples, teachers can move beyond strict adherence to the method book, and change their focus from notational literacy to authentic musicianship.

Thesis (M.MusEd.)--Conservatory of Music. University of Missouri--Kansas City, 2007. / "A thesis in Music Education." Typescript. Advisor: Joseph Parisi. Vita. Title from "catalog record" of the print edition Description based on contents viewed Jan. 8, 2008. Contains bibliographical references (leaves 137-144). Online version of the print edition.

The purpose of the study was to determine the composition and characteristics of employer and employee negotiating teams during initial stages of legislated collective bargaining for public schools in Indiana. Study participants included superintendents and trustees and 204 presidents of identified exclusive representatives of bargaining units of Indiana school systems.Participants responded relative to the number, race, sex, previous negotiating experience, and professional employment positions held by negotiating team members; positions held in professional employment, previous negotiating experience, race, and sex of negotiating team spokesmen; remuneration of negotiating team members and spokesmen; and extent of use, position held on negotiation teams, basis of selection, previous negotiating experience, amount and basis of remuneration of outside experts in negotiations.Findings of the study were:1. Teacher negotiating teams were larger than teams representing boards of education. Teachers' teams increased in number as pupil enrollment of school systems increased.2. Almost all negotiating teams had one individual identified as negotiating team spokesman.3. School board member participation on employer negotiating teams increased as pupil enrollment of school systems decreased.4. Slightly more than 25 per cent of all superintendents participated on employer negotiating teams. Only one superintendent served as spokesman in school districts serving 12,000 or more students.5. The number of principals serving as spokesman increased as the pupil enrollment of school systems decreased.6. The number of business managers serving as spokesmen increased as the pupil enrollment of school systems increased.7. Assistant Superintendents for Personnel and/or Directors of Employee Relations served as spokesman only in districts serving 6,000 or more pupils.8. Attorneys served as outside experts on one-half of employer negotiating teams.9. Negotiating team membership reflected all segments of employer and employee groups.10. Slightly less than one-half of team spokesmen reported they had no previous negotiating experience. Almost 100 per cent of team spokesmen for school systems with 6,000 or fewer students were inexperienced.11. The years of previous negotiating experience of outside experts increased as the pupil enrollment of school systems increased.12. Almost 100 per cent of employer negotiating team spokesmen outside experts and team members were white males.13. Slightly more than three-fourths of employee negotiating team spokesmen were males.14. Employee negotiating team membership was composed of about one-half white males and one-half white females.15. Almost 100 per cent of the outside experts and spokesmen for employee negotiating teams were white males.16. Remuneration of outside experts ranged from $23,000 to $500. Per diem compensation ranged from $300 to $100. Per hour compensation ranged from $55 to $20.17. Almost one-half of employer team outside experts were paid on a per hour basis plus expenses.18. Outside experts serving employee negotiating teams were reimbursed in salary by state and/or national teacher organizations.Size of school district is a major factor in determining the composition and characteristics of negotiating teams.The amount or basis of remuneration paid to outside experts, spokesmen, or team members is not positively related to size of school system.Large amounts of tax dollars are being expended to carry out mandated negotiations.Employer negotiations are conducted by white males. Employee negotiations spokesmen are predominately white males.As of 1975 the status of negotiations in Indiana is one of attempting to implement a process; therefore, the level of sophistication in negotiations is minimal.Management personnel have lacked adequate preparation for negotiations and consequently have turned to outside experts until competent in-house negotiators are developed.Teacher groups rely heavily upon assistance provided through national networks of teacher organizations.

Thesis (master's in Music)--University of Wisconsin--Milwaukee, 2001. / Includes bibliographical references (leaves 55-59).

Thesis (D.M.A.)--University of North Texas, 2008. / System requirements: Adobe Acrobat Reader. Accompanied by 4 recitals, recorded Sept. 24, 2007. Includes bibliographical references (p. 107-108).

Diss.--Berlin--Freie Univ., 2005. / Bibliogr. p. 247-265.

Not available / text

Chemnitz, Techn. Univ., Magisterarb., 1998.

Thesis--Kent State University. / Photocopy of typescript. Ann Arbor, Mich. : University Microfilms International, 1978. -- 21 cm. Includes bibliographical references (leaves [183]-219).

De nombreuses problématiques d'apprentissage artificiel peuvent être modélisées grâce à des fonctions à valeur vectorielles. Les noyaux à valeurs opérateurs et leur espace de Hilbert à noyaux reproduisant à valeurs vectorielles associés donnent un cadre théorique et pratique pour apprendre de telles fonctions, étendant la littérature existante des noyaux scalaires. Cependant, lorsque les données sont nombreuses, ces méthodes sont peu utilisables, ne passant pas à l'échelle, car elle nécessite une quantité de mémoire évoluant quadratiquement et un temps de calcul évoluant cubiquement vis à vis du nombre de données, dans leur implémentation la plus naïve. Afin de faire passer les noyaux à valeurs opérateurs à l'échelle, nous étendons une technique d'approximation stochastique introduite dans le cadre des noyaux scalaires. L'idée est de tirer parti d'une fonction de redescription caractérisant le noyau à valeurs opérateurs, dont les fonctions associées vivent dans un espace de dimension infinie, afin d'obtenir un problème d'optimisation linéaire de dimension finie. Dans cette thèse nous développons dans un premier temps un cadre général afin de permettre l'approximation de noyaux de Mercer définis sur des groupes commutatifs localement compacts et étudions leurs propriétés ainsi que la complexités des algorithmes en découlant. Dans un second temps nous montrons des garanties théoriques en bornant l'erreur commise par l'approximation, avec grande probabilité. Enfin, nous mettons en évidence plusieurs applications des Représentations Opérateurs Aléatoires de Fourier (ORFF) telles que la classification multiple, l'apprentissage multi-tâche, la modélisation des séries temporelles, la régression fonctionnelle et la détection d'anomalies. Nous comparons également ce cadre avec d'autres méthodes de la littérature et concluons par des perspectives à moyen et long terme. / Many problems in Machine Learning can be cast into vector-valued approximation. Operator-Valued Kernels and vector-valued Reproducing Kernel Hilbert Spaces provide a theoretical and practical framework to address that issue, extending nicely the well-known setting of scalar-valued kernels. However large scale applications are usually not affordable with these tools that require an important computational power along with a large memory capacity. In this thesis, we propose and study scalable methods to perform regression with Operator-Valued Kernels. To achieve this goal, we extend Random Fourier Features, an approximation technique originally introduced for scalar-valued kernels, to Operator-Valued Kernels. The idea is to take advantage of an approximated operator-valued feature map in order to come up with a linear model in a finite-dimensional space. This thesis is structured as follows. First we develop a general framework devoted to the approximation of shift-invariant MErcer kernels on Locally Compact Abelian groups and study their properties along with the complexity of the algorithms based on them. Second we show theoretical guarantees by bounding the error due to the approximation, with high probability. Third, we study various applications of Operator Random Fourier Features (ORFF) to different tasks of Machine learning such as multi-class classification, multi-task learning, time serie modelling, functionnal regression and anomaly detection. We also compare the proposed framework with other state of the art methods. Fourth, we conclude by drawing short-term and mid-term perspectives of this work.