An academic approach to building websites with html and css. This course is aimed at beginning programmers and self-taught web developers who would like to cement their skills. Topics include structural html5, css, and introductory javascript/jquery (if time permits).

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering with a focus on real-world applications in a variety of computing contexts (desktop/cluster/cloud). Requires the use of Python.

Introduction to core machine learning models and algorithms for classification, regression, dimensionality reduction and clustering with a focus on real-world applications in a variety of computing contexts (desktop/cluster/cloud). Requires the use of Python.

Satisfies the Junior Year Writing requirement. The impact of computers on modern society.

Development of mathematical reasoning skills for problems that involve uncertainty. Counting and probability, probabilistic reasoning, Naive Bayes classifiers, Monte Carlo simulation, Markov chains, Markov decision processes, classical game theory, and introduction to information theory.

Large-scale software systems like Google - deployed over a world-wide network of hundreds of thousands of computers - have become a part of our lives. These are systems success stories - they are reliable, available ("up" nearly all the time), handle an unbelievable amount of load from users around the world, yet provide virtually instantaneous results.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

An in-depth introduction to the main models and concepts of the mathematical theory of computation, including computability, complexity, and logic. Prerequisites: an undergraduate course in automata theory and formal languages such as COMPSCI 501 or permission of instructor.

Knowledge discovery is the process of discovering useful regularities in large and complex data sets. The field encompasses techniques from artificial intelligence (representation and search), statistics (inference), and databases (data storage and access). When integrated in to useful systems, these techniques can help human analysts make sense of vast stores of digital information.

Introduction to the efficient management of large-scale data. Principles for representing information in the relational model and semi-structured data models (XML, JSON); query languages for analyzing and manipulating data (SQL and others); core systems principles for data management (file organizations, query optimization, indexing, distributed data processing, concurrency control). The colloquium will focus on advanced topics and recent research topics related to information management and data science.