Data structures course using the Java programming language. Basic mathematical, logical, and programming concepts relevant to description and manipulation of information structures such as arrays, lists, trees, graphs, and files; the underlying principles of algorithm design and analysis applied to sorting and searching problems. Prerequisite: grade of C or better in E&C-ENG 122, 201 or equivalent.

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.

Introduction to computer communication networks and protocols. Fundamental concepts in the design and analysis of computer networks. Topics include: layered network architectures, applications, network, programming interfaces, transport, congestion, routing, data link protocols, local area networks, emerging high-speed networks, network management, and network security. Examples drawn from the Internet (e.g., TCP, UDP, and IP) protocol suite.

A brief introduction to the C programming language for students with a good working knowledge of Java and data structures. This course is good preparation for CMPSCI 230 and courses that use C and C++. Prerequisites: CMPSCI 187 or ECE 242 or permission of instructor. This course is for CMPSCI minors and majors only, but it does not count towards either degree.

This class is an in-depth introduction to systems, focusing on principles of system design that cross-cut numerous systems artifacts, including operating systems, databases, runtime systems, and architecture. We will cover all levels of the "system stack", from chips to distributed systems. This course may be used to satisfy systems core requirements.

Advanced Topics in Computer Science Master's Project: Advanced research project in Computer Science.

In this course we peel away the layers of abstraction and look at how switches become logic circuits, how logic circuits do math, and how programs really execute. We will wire up some simple examples of logic, move on to programming a minimalist simulation of a computer, and gradually build up to the point where we can appreciate how a C program is translated into machine code and what really happens when it executes. We will also see the impact of hidden acceleration mechanisms like caches, pipelines, and branch predictors.

The Course explores key concepts of artificial intelligence, including state-space and heuristic search techniques, game playing, knowledge representation, automated planning, reasoning under uncertainty, decision theory and machine learning. We will examine how these concepts are applied in the context of several applications.

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.

Mathematical methods for dealing with uncertainty. Basic counting problems. Discrete probablitlity, expected value and variance. Random number generators and Monte Carlo simulation. Bayesian reasoning, naive Bayes classifiers. Markov processes. Programming projects.