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.

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.

This course introduces the fundamental concepts of two-dimensional (2D) and three-dimensional (3D) computer graphics. It covers the basic methods needed to model, render, and animate 3D objects. Topics include raster displays, line drawing, affine and perspective transformations, windows and viewpoints, clipping, visibility, illumination models, reflectance models, radiometry, curves and surfaces, shading, texture mapping, ray tracing, graphic toolkits, animation, and 3D printing.

Introduction to formal language theory. Topics include finite state languages, context-free languages, the relationship between language classes and formal machine models, the Turing Machine model of computation, theories of computability, resource-bounded models, and NP-completeness. It is recommended that students have a B- or better in COMPSCI 311 in order to attempt COMPSCI 501.

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) and distributed data processing paradigms.

Practical experience in the management of software development projects. Managing people, coaching, mentoring, planning, scheduling, evaluation, negotiation, and expectation management are explored. The focus is on the power of communication.