Become a Leader in Artificial Intelligence

This course offers a survey of current database approaches and systems, as well as the principles of design and use of these systems. It covers query language design and implementation constraints, as well as applications of large databases. Course topics include a survey of file structures and access techniques, as well as the use of a relational database management system to implement a database design project.

This course includes topics on algorithm design techniques; methods for evaluating algorithm efficiency; data structure specification and implementation; and applications to fundamental computational problems in sorting and selection, graphs and networks, scheduling and combinatorial optimization, computational geometry, and arithmetic and matrix computation. It also introduces students to parallel algorithms and to computational complexity and a survey of NP-complete problems.

This course covers theoretical and practical aspects of operating systems, including an overview of system software, time-sharing and multiprogramming operating systems. Students also learn about network operating systems and the internet, virtual memory management, interprocess communication and synchronization, file organization, and case studies.

This course introduces the state of the art in uniprocessor computer architecture, with a focus on the quantitative analysis and cost-performance trade-offs in instruction set, pipeline and memory design. Topics include quantitative analysis of performance and hardware costs, instruction set design, pipeline, delayed branch, memory organization, and advanced instruction-level parallelism.

This course introduces students to basic principles and current research topics in artificial intelligence. It covers the formal representation of real-world problems; the search of problem spaces for solutions; and the deduction of knowledge in terms of predicate logic, nonmonotonic reasoning and fuzzy sets. Students also learn how to apply these methods to important areas of artificial intelligence, including expert systems, planning, language understanding, machine learning, neural networks, computer vision and robotics.

This course introduces the processes of learning from data, providing an overview of a number of machine learning techniques used in analytics, including pre-processing, visualization, classification and regression. It covers classic and contemporary learning techniques, with emphasis on artificial neural networks and deep learning methods. Students also complete hands-on projects using state-of-the art tools. Class examples and assignments come from the programming language Python.

This course equips students with the practical skills necessary to develop mobile applications that take advantage of the myriad sensing and control capabilities of modern smartphones. Focuses on interfacing with phone hardware, efficient computing on the phone and in the cloud using virtualized servers, and efficient analysis of the peripheral sensor streams of today’s smartphones. Students integrate real-time control and/or automation using a third-party hardware platform to interface with the mobile platform.

This course surveys current database approaches and systems, and the principles of design and use of these systems. Covers query language design and implementation constraints, and applications of large databases. Includes a survey of file structures and access techniques. Also, the use of a relational database management system to implement a database design project. Prerequisite: Knowledge equivalent to CS 1341.

This course introduces data mining techniques (classification, association analysis, and cluster analysis) used in analytics. All material covered is reinforced through hands-on experience using state-of-the art tools to design and execute data mining processes. Prerequisites: Knowledge equivalent to CS 1342, CS 4340/EMIS 3340/STAT 4340, EMIS 3309. Reserved for Lyle majors.

This course introduces the field of information retrieval, with an emphasis on its application in Web search. Also introduces the basic concepts of stemming, tokenizing and inverted indices, text similarity metrics, and the vector-space model. Students study popular Web search engines and apply the concepts in several Java-based projects. Prerequisite: Knowledge of CS 3353 or permission of instructor.

This course focuses on higher-level artificial intelligence techniques for problem-solving guided by domain-specific knowledge. Topics include the use of planning systems, heuristic rule-based systems, model-based systems, learning networks, and semantic technologies. Prerequisite: CS 7320.

This course introduces students to the principles and motivation behind forms of machine learning with emphasis on neural networks. Survey of important topics and current areas of research, including the use of deep learning for training massive networks. Prerequisite: CS 7324 or permission of instructor.

This course covers state-of-the-art methods for natural language processing. After an introduction to the basics of syntax, semantic, and discourse analysis, the focus shifts to the integration of these modules into complex natural-language processing systems. In addition to natural language understanding, the course presents advanced material on lexical knowledge acquisition, natural language generation, machine translation, and parallel processing of natural language. Prerequisite: CS 7320.

This course explores logic-based computing and logic programming. Introduces fundamentals of logic programming and covers basic techniques for solving problems in Prolog, including nondeterministic programming, incomplete data structures, definite clause grammars, and meta interpreters. Examines implementation of a logic programming system as a generalization of both traditional programming language systems and traditional databases. Prerequisites: CS 2341, CS 3342.

This course provides a review of several data mining topics and an in-depth technical discussion of advanced data mining techniques. In addition, research methods applied in the field will be studied. Prerequisite: CS 7331.

This course examines the techniques used to store and retrieve unformatted/textual data. It examines the current research topics of data mining, data warehousing, digital libraries, hypertext, and multimedia data. Prerequisite: CS 7330.

There is an associated application fee of $75.

A bachelor’s degree in one of the quantitative sciences, mathematics or computer science or in one of the engineering disciplines, a minimum GPA of 3.0 on a 4.0 scale in your undergraduate studies, and a minimum of one year of college-level calculus are required. Please note that applicants with undergraduate degrees in disciplines other than computer science may be admitted to the program but may be required to take articulation coursework and/or satisfy the competency requirement.

Upload your unofficial transcripts in the Academic History quadrant of the application. Please note, we will accept unofficial transcripts for the application, but we will request official transcripts once you are admitted.

• International transcripts need to be in English
• No transcript evaluation is needed

Applicants with 2 years of relatable industry work experience may request a waiver for the GRE or submit their official GRE general graduate school admission test scores.

There are no letters of recommendation for this program. This program requires 2 professional references.

The student must submit contact information for 2 professional references; 1 of which should be a current or former supervisor who can speak to the applicant’s character and work experience and ability to be successful in a program.

Applicants with 2 years of relatable industry work experience may request a waiver for the GRE or submit their official GRE general graduate school admission test scores.

Only those who can provide proof of study from either a US, Australian, New Zealand or UK institution will have this requirement waived.