Course Schedule

The course will provide a general overview of the TESL profession covering prominent theories, methodologies, and issues in the field. Coursework will cover the major methods, including Grammar-Translation, the Direct Method, Audiolingualism, and Communicative Language Teaching. In addition, issues of learner variables, motivation, and contexts of teaching and learning will also be addressed. Students will participate in mock lessons, tutoring sessions, and observations.

Graduate level requirements include a 12-15 page research paper with bibliography of at least eight sources.

Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.

The course will provide a general overview of the TESL profession covering prominent theories, methodologies, and issues in the field. Coursework will cover the major methods, including Grammar-Translation, the Direct Method, Audiolingualism, and Communicative Language Teaching. In addition, issues of learner variables, motivation, and contexts of teaching and learning will also be addressed. Students will participate in mock lessons, tutoring sessions, and observations.

Graduate level requirements include a 12-15 page research paper with bibliography of at least eight sources.

Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.

Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.