Please complete the anonymous on-line survey for your course. Thank you. In order to complete your TEXTS course, you are required to take part in the end-of-course survey at this link. There are 10 questions and the survey will take approximately two minutes to complete.
The survey is completely anonymous. Students who were absent for any of the tests can take their tests on. Vai al contenuto principale. Pannello laterale. Ospite Login. Cerca corsi Vai. Blocchi del corso. Professor Brownlees. Professor Samson.
FAQ Lettorato. The Grammar Test. Lettorato course: The Study and Production of Texts. Tutor Help Service. Plagiarism guidelines. Teacher Sections.
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An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THIS is used solely as a reading device such as Nuvomedia's Rocket eBook. Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook or other reading material from a Web site such as Barnes and Noble to be read from the user's computer or reading device.Objectives of the course:.
The course aims to provide the students with the necessary linguistic competences to easily read and understand all sorts of scientific texts written in English. The course will be based on the notional-functional approach aimed at developing the many aspects of L2: functions and notions, lexis and registers, communicative and cognitive competencies, receptive and productive abilities specifically written. The goal set is the reinforcement of the comprehension of the written language, the enhancement of the vocabulary ESP and the acquisition of the B2 Level grammar.
Description of contents:. Introduction to linguistic structures from a morphological, syntactic and semantic point of view within specific situations and English cultural contexts. Development of diversified communicative and analytical skills in order to understand both written and oral English. Development of reading skills on scientific texts.
A number of activities such as summarizing, paraphrasing, text analysis and instructions formulation will be provided. Mixed conditionals. Skills to be developed and expected learning outcomes:. Develop communicative abilities, reflect on linguistic structures and detect the differences between L1 and L2.
Enhance logical and critical process of thoughtful reflection, improve the method of study, the use of memory, metacognitive abilities and self-confidence. Promote autonomous work and interaction. Show openness, curiosity and interest in other cultures. Final evaluation:. The final evaluation will be written following the Multiple Choice format. It will be divided into two parts: the one assessing grammar, the other comprehension of scientific texts. Vai al contenuto principale. Pannello laterale.
Non sei collegato. Description of contents: Introduction to linguistic structures from a morphological, syntactic and semantic point of view within specific situations and English cultural contexts.Sono previste esercitazioni di analisi sintattica e pragmatica di testi nella lingua e cultura di arrivo. The course will introduce the principles of English syntax, focusing on the structure of simple and complex sentences, observing the form and function of phrase, clause and sentence, as well as the syntactic relatedness, sentence information and variations in the information structure.
A selection of English texts will be considered for text analysis by analysing the pragmatic aspects of discourse. The course aims at developing an awareness of the sentence structure in English syntax and how information is conveyed and varied in speech, taking account of pragmatics principles. A selection of English texts will be considered for text analysis. Le esercitazioni di Lettorato sono parte integrante del corso.
English Language: Fundamentals of syntax and pragmatics Laura DiamantiThe course will introduce the principles of English syntax, focusing on the structure of simple and complex sentences, observing the form and function of phrase, clause and sentence, as well as the syntactic relatedness, sentence information and variations in the information structure. Scott Allan4th Ed. English Language. Sentence; Pragmatics. Intermediate Level of Language Proficiency Lettorato : Reference texts and materials will be provided in class.
Lectures, exercises and follow-up activities.
Esame orale sui contenuti del corso, includendo gli aspetti pragmatici del discorso. Written exam on English syntax, along with the Lettorato written assessment on General English, prior to the oral exam. Oral exam on the contents of the course, including the pragmatic aspects of discourse. Form di ricerca Cerca.
Ateneo Didattica Ricerca Terza Missione. Home Didattica Offerta formativa. Attivita' formativa monodisciplinare. Codice dell'attivita' formativa:. Anno accademico di regolamento:. Anno di erogazione:. Tipologia di insegnamento:. Settore disciplinare:.
Anno di corso:. Ore di attivita' frontale:.
Ore di studio individuale:. Tipo di esame:. Modalita' di verifica dell'apprendimento:.Office Hours December:. This course focuses on the language skills needed to communicate more appropriately and accurately in professional fields. It also aims to raise awareness of issues related to cultural differences in work environments.
Students will also practice speaking and listening skills needed in situations such as work interviews, on the telephone, meetings, negotiations and other formal interactions. The work requirements include prompt completion of assigned homework, simulation of a job application letter, CVjob interview and a final written test.
This is a single semester course and attendance is obligatory. Vai al contenuto principale. Pannello laterale. Ospite Login. Cerca corsi Vai. Blocchi del corso. Sign-ups for second-semester lettorati. British Culture and Language. English for Professional Communication. Language and Culture in U. Language in Poetry from Africa and South Asia. The Street. Translation English Italian. The Dialects of the City: aspects of Psychogeography.
Language and Culture in Canada. Languages and Work File. CVs and AI File.By default, only the 20 most recent topic models will be returned. You can get your list of topic models directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your topic models. Time Series Last Updated: Friday, 2017-10-27 12:23 A time series model is a supervised learning method to forecast the future values of a field based on its previously observed values.
It is used to analyze time based data when historical patterns can explain the future behavior such as stock prices, sales forecasting, website traffic, production and inventory analysis, weather forecasting, etc. A time series model needs to be trained with time series data, i. BigML implements exponential smoothing to train time series models. Time series data is modeled as a level component and it can optionally include a trend (damped or not damped) and a seasonality components as explained below:Forecast equation Level equation Forecast equation Level equation Trend equation Forecast equation Level equation Damped trend equation Forecast equation Level equation Trend equation Seasonality equation The different components can have variations, e.
As a result of combining the different variations for each component, several models can be trained for a given objective field. Note that BigML excludes certain combinations for numerical stability reasons such as additive errors with multiplicative trends or multiplicative error and trend with additive seasonality. BigML computes four different performance measures to select the best model for a given objective field. You can create a time series model selecting one or several fields from your dataset to use as objective fields to forecast their future values.
You can also list all of your time series. This can be used to change the names of the fields in the time series with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.
Example: 100 name optional String,default is dataset's name The name you want to give to the new time series. The type of the field must be numerical. Non-numeric fields will be ignored, and if not present, the right-most valid field in the dataset will be used. The period needs to be set taking into account the time interval of your instances and the seasonal frequency. For example, for monthly data and annual seasonality, the period should be 12, for daily data and weekly seasonality, the period should be 7.
It can take values from 0 to 60. If the period is set to 1, there is no seasonality. If the period is 0, or not given, BigML will automatically learn the period in your data. The range of successive instances to build the time series. Multiplicative seasonality models are only available when the objective field has strictly positive values (greater than 0). Example: 2 tags optional Array of Strings A list of strings that help classify and index your time series. If absent, the first datetime field in the dataset whose values are continuously either decreasing or increasing.Submit any pending changes before refreshing this page.
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