Ruslan Russian 1 Coursebook

This is a communicative Russian course for adults and young people, from beginners to intermediate level.

Russian: From Intermediate to Advanced is a vibrant and modern course designed to help students achieve advanced proficiency in Russian. Offering a flexible modular approach structured around contemporary themes, the course further develops reading, listening, speaking, and writing skills while also expanding the student’s cultural literacy. Key features include: Structured chapters presenting a wide assortment of readings that include blogs, forums and surveys as well as opinion pieces and commentaries. Each text is accompanied by assignments with increasing levels of difficulty. Authentic and up-to-date readings, video and audio excerpts covering a range of relevant social and cultural topics, including Demography, Youth Culture, Politics and Society, Economics and Globalization. Video clips from news programs that are used not only to develop listening comprehension but also introduce students to contemporary Russian society. Particular attention to helping students acquire advanced vocabulary and the ability to converse, discuss and argue about issues with extended paragraph-length discourse. Special focus on the development of strong listening and reading comprehension skills, ensuring that students understand the ideas and supporting details in narrative and descriptive texts and connected discourse. A free companion website at http://www.russian.ucla.edu/AdvancedRussian/ offering student and instructor video and audio resources, sample syllabi and tests as well as additional materials. Written by a highly experienced author team that has co-authored the first year Russian textbook Beginner’s Russian (2010) and the second-year textbook V Puti (2005). Russian: From Intermediate to Advanced will be an essential resource for undergraduate students in their third and fourth year of Russian language study. It is also suitable for heritage learners of Russian who have mastered literacy and are familiar with the grammatical structure of Russian.

The balalaika is most often associated with Russian folklore. In this comprehensive method book, concert and recording artist Bibs Ekkel shares his extensive knowledge of the balalaika as taught in Russia today. This 160-page book is divided into four sections: an extensive Foreword, the Tutor, A Brief History of the Balalaika, and a Repertoire Section. Unlike many Mel Bay books, the Complete Balalaika Book does not begin with the rudiments of music notation. Rather, the 20-page Foreword offer insights on the tunings of all the instruments in the balalaika family, hand positions, string sources and height adjustment, and chord shapes. the author even describes how to make a bass balalaika pick from shoe leather. the Tutor or instruction segment is written for the 'prima balalaika' which is correctly played with the fingers only rather than with a pick. the Tutor jumps right in with exercises and tunes in quarter, eighth, and sixteenth-note values with occasional dotted notes and syncopation. Perhaps half of the music in this section is shown in both standard notation and tablature. A glossary of musical signs and Russian musical terns is included. A Brief History of the Balalaika is well written and nicely illustrated. Very few Mel Bay books contain such a comprehensive historical overview of the subject instrument. the Repertoire Section offers a valuable resource of Eastern European folk tunes. All but two selections in this section are written in both notation and tab with suggested guitar chords, the exceptions being an arrangement for balalaika and piano, and a balalaika orchestra score. More than simple melody chord leadsheets, these are the author's own arrangements of tunes for solo balalaika and balalaika orchestra. A companion CD with selections performed by the author completes this highly effective instructional package.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.