An introduction to the methods and tools of software engineering; software life cycle; specification and design of software, software testing, cost and effort estimation; laboratory exercises with design, testing, and other tools.

File organization and index structures; object-oriented database languages; the relational database model with introductions to SQL and DBMS; hierarchical models and network models with introductions to HDDL, HDML, and DBTG Codasyl; data mining; data warehousing; database connectivity; distributed databases; the client/server paradigm; middleware, including ODBC, JDBC, CORBA, and MOM.

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.

Important concepts, models, algorithms, abstractions, and implementation aspects of concurrent and parallel programs. Topics include: techniques used to describe concurrent programs (e.g., threads, events, co-routines, continuations), abstractions for shared-memory and message-passing programs, relaxed memory models, livelock and deadlock detection, lock-free algorithms, data races and atomicity, scheduling techniques, process calculi, and software transactions.

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.

Preliminaries: errors and testing; software quality, requirements, behavior, and correctness; testing, debugging, verification; control flow graphs, dominators; types of testing; Test selection: from requirements, finite state models, and combinatorial designs; regression testing and test minimization; Test adequacy assessment: control and data flow; mutation based; testing tools.

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.

An in-depth examination of relational database systems including theory and concepts as well as practical issues in relational databases. Modern database technologies such as object-relational and Web-based access to relational databases. Conceptual design and entity relationship modeling, relational algebra and calculus, data definition and manipulation languages using SQL, schema and view management, query processing and optimization, transaction management, security, privacy, integrity management.

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.

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.

This course teaches important concepts and knowledge of information retrieval for managing unstructured data such as text data on Web or in emails. At the same time, students will be exposed to a large number of important applications. Students in the course will get hands on experience from homework and a course project. The first part of the course focuses on general concepts/techniques such as stemming, indexing, vector space model, and feedback procedure. The second part of the course shows how to apply the set of techniques on different applications such as Web search, text categorization, and information recommendation.

Software for embedded systems; programming with extreme constraints on memory, storage, and processing power; programming models, including synchronous, asynchronous, and concurrent; programming systems that run forever; use of Finite State Machines (FSMs); review of embedded hardware and available boards; asynchronous and synchronous serial connections (e.g., RS232 and SPI); General Purpose I/O (GPIO) pins; relays, servos, and other peripherals; design for low power; battery characteristics; systems that communicate over computer networks and the Internet; current industry practices; design of embedded projects; working in teams.