The theory and practice of programming language translation, compilation, and run-time systems, organized around a significant programming project to build a compiler for a simple but nontrivial programming language. Modules, interfaces, tools. Data structures for tree languages. Lexical analysis, syntax analysis, abstract syntax. Symbol tables, semantic analysis. Translation, intermediate code, basic blocks, traces. Instruction selection, CISC and RISC machines. Liveness analysis, graph coloring register allocation. Supplemental material drawn from garbage collection, object-oriented languages, higher-order languages, dataflow analysis, optimization, polymorphism, scheduling and pipelining, memory hierarchies.

Introduction to operating systems. Computer system and operating system architectures, processes, inter-process communication, inter-process synchronization, mutual exclusion, deadlocks, memory hierarchy, virtual memory, CPU scheduling, file systems, I/O device management, security.

This course will introduce students to the field of data mining and machine learning, which sits at the interface between statistics and computer science. Data mining and machine learning focuses on developing algorithms to automatically discover patterns and learn models of large datasets. This course introduces students to the process and main techniques in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation.

Techniques for analyzing the time and space requirements of algorithms. Application of these techniques to sorting, searching, pattern-matching, graph problems, and other selected problems. Brief introduction to the intractable (NP-hard) problems.

Undergraduate-level introduction to computer networks and data communication. Low-level details of media, signals, and bits: time division and frequency division multiplexing; encoding; modulation; bandwidth, throughput, and noise. Packet transmission: Local Area Network (Ethernet, FDDI) and Wide Area Network technologies (ATM); wireless networks; network interconnection with repeaters, bridges, and switches; DSU/CSU; xDSL and cable modems. Internetworking: router-based architecture; IP addressing; address binding with ARP; datagram encapsulation and fragmentation; UDP and TCP, retransmission; protocol ports; ICMP and error handling. Network applications: client/server concept; port demultiplexing; program interface to protocols (API); use by clients and servers; domain name system; TELNET; Web technologies including HTTP, CGI, Java; RPC and middleware; network management.

Advanced concepts and techniques of computer graphics. The course covers, in complete detail, going from a scene representation to a raster image without using OpenGL or other graphics packages. The course develops a complete graphics implementation in which the students implement every aspect of the graphics pipeline. This involves a substantial software project in C/C++.

The course offers an introduction to the fundamentals principles, design strategies, and techniques needed to visually communicate, explore, and analyze data. The course focuses primarily on the visual representation of inherently non-spatial data (e.g., tables and spreadsheets, graphs and networks, trees, text, and time series), but also considers the visualization of maps and of data in geospatial context.

Concepts for structuring data, computation, and whole programs. Object-oriented languages, functional languages, logic- and rule-based languages. Data types, type checking, exception handling, concurrent processes, synchronization, modularity, encapsulation, interfaces, separate compilation, inheritance, polymorphism, dynamic binding, subtyping, overloading, beta-reduction, unification.

Any intelligent robot system interacting with our environment needs to have perception, planning, and control methods in its cognition process. The perception module outlines the robot’s procedures to gather and interpret sensory observations into world models. The underlying planning and control modules use those world models to plan robot behaviors and their interaction with our natural environments. Therefore, this course will cover the fundamental topics in robot perception, planning, and control to design general-purpose robot cognition algorithms. Overall, this course is divided into four modules: Robot perception: This covers fundamental techniques needed for robot localization and mapping from raw 3D sensory data. Robot planning: This module will discuss robot behavior planning techniques such as A*, RRT*, and trajectory optimization. Robot Control: This introduces basic control techniques such as PID controller to execute the robot’s planned behaviors in the real world. Robot Learning: This part will briefly introduce machine learning techniques for robot decision-making and control.

Students are expected to spend at least three hours per week gaining experience with artificial intelligence systems and developing software. Basic problem-solving strategies, heuristic search, problem reduction and AND/OR graphs, knowledge representation, expert systems, generating explanations, uncertainty reasoning, game playing, planning, machine learning, computer vision, and programming systems such as Lisp or Prolog.