Programming Intensive Options

ISTA 457: Neural Networks

Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.

ISTA 450: Artificial Intelligence

The methods and tools of Artificial Intelligence used to provide systems with the ability to autonomously problem solve and reason with uncertain information. Topics include: problem solving (search spaces, uninformed and informed search, games, constraint satisfaction), principles of knowledge representation and reasoning (propositional and first-order logic, logical inference, planning), and representing and reasoning with uncertainty (Bayesian networks, probabilistic inference, decision theory).

ISTA 455: Applied Natural Language Processing

Most of web data today consists of unstructured text. This course will cover the fundamental knowledge necessary to organize such texts, search them a meaningful way, and extract relevant information from them.

ISTA 355: Introduction to Natural Language Processing

Natural language processing (NLP) is the study of how we can teach computers to use language by extracting knowledge from text, and then use that knowledge in some meaningful way.  In this introductory course, we will examine the fundamental components on which natural language processing systems are built, including frequency distributions, part of speech tagging, syntactic parsing, semantics and analyzing meaning, search, introductory information and relation extraction, and structured knowledge resources.  We will also examine pragmatic concerns in processing raw text from real-world sou

ISTA 331: Principles and Practice of Data Science

This course surveys the techniques central to the modern practice of extracting useful patterns and models from large bodies of data and the theory behind these techniques.  Students will learn the purpose, power, and limitations of models, with concrete examples from business and science.  Course subject matter may include classification and regression, supervised segmentation and decision trees, similarity/distance metrics and recommender systems, clustering and nearest neighbors, support vector machines, understanding and avoiding overfitting, natural language processing and sentiment an

ISTA 350: Programming for Informatics Applications

This course will provide an introduction to informatics application programming using the python programming language and applying statistical concepts from a first semester statistics course.

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