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NATURAL LANGUAGE PROCESSING WITH PYTHON
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This course will introduce algorithmic techniques from Machine
Learning (ML) for identifying useful and relevant patterns,
associations, and relationships in and from natural language and text
data in order to automate the process of learning from these types of
data. The student will learn how ideas and methods from probability
theory, mathematical statistics, learning theory, optimization, and
computational complexity theory are combined to design these
algorithmic techniques. Fundamental methods from Natural Language
Processing (NLP) such as word and text embeddings, classification,
supervised learning, generalization theory, and the model reduction
will be introduced. Methods for query relevance assessment and
relevance-ranking will be discussed. Specific examples of industry and
business use cases for NLP will be given in the course.
The student is required to work on course projects by using modern
data analysis software and cases studies. This course will focus on
the implementation of NLP algorithms using the Python language.
COURSE OBJECTIVES
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To learn how computational methods and techniques are employed in
Natural Language Processing and text mining and to learn the
analytical, theoretical, and intuitive ideas that underpin them.
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To understand and become familiar with the implementation details of
NLP algorithms.
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To gain hands-on experience with NLP tools in the Python language.
Week 1: Natural Language Processing Overview and Text Representation
Week 2: Bag-of-Words Approach and Word Embeddings
Week 3: ML classification algorithms for NLP and text mining
Week 4: Introduction to Artificial Neural Networks for NLP
Week 5: Support Vector Machines for NLP
Week 6: Ensemble learning, Boosting, and Bayesian ML for text mining
Week 7: Testing, Verification, Validation, and Visualization for text
mining
Week 8: Information retrieval and text ranking
Instructor
Daniel Zanger, Ph.D
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Daniel Zanger, Ph.D., has over 20 years of experience in both industry
and the federal government, working extensively in the fields of
theoretical and applied machine learning, data analysis, optimization,
statistical database privacy, cryptology, quantum computing, and
others. He has applied techniques from these fields to problems in
such areas as text mining, image processing, operations research, and
multi-sensor fusion. Dr. Zanger has authored numerous publications in
refereed journals and conference proceedings in various technical
fields including mathematics (partial differential equations),
probability theory, information retrieval, statistical learning theory
(applied to finance), operations research, and database privacy. He
holds a Ph.D. in Mathematics from the Massachusetts Institute of
Technology (MIT) as well as a B.A. (with Highest Honors), also in
Mathematics, from the University of California at Berkeley
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25/05/2019 Last update