This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.
This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done on the MIT Server in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.
This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing. The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing. This subject qualifies as an Artificial Intelligence and Applications concentration subject.
Seminar in real-time language comprehension. Models of sentence and discourse comprehension from the linguistic, psychology, and artificial intelligence literature, including symbolic and connectionist models. Ambiguity resolution. Linguistic complexity. The use of lexical, syntactic, semantic, pragmatic, contextual and prosodic information in language comprehension. The relationship between the computational resources available in working memory and the language processing mechanism. The psychological reality of linguistic representations.
This course is broad, covering a wide range of topics that have to do with the post-PC era of computing. It is a hands-on project course that also includes some foundational subjects. Students will program iPAQ handheld computers, cell phones (series 60 phones), speech processing, vision, Cricket location systems, GPS, and more. Most of the programming will be using Python®, but Python® can be learned and mastered during the course. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 5508 (Pervasive Computing).
You will write code to compute the autocorrelation or autocovariance of an input signal. Then you connect a microphone to the DSP and write code to detect the beginning of a speech segment. Finally, you will combine the two programs and compare results with MATLAB.
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