An introductory course on computer programming, in which the students design, implement, test, and debug programs for computational problems using Python programming language. This course emphasizes program design process, object-oriented software development approach, and practical programming skills that translate to programming in other modern languages. Assignments include practical problems drawn from various fields (e.g. biology, linguistics, graphics, and games). Open to high-school students only, no prerequisites. Credits don’t count toward Coordinate Major in Computer Science.

An introductory practice-oriented course on computer programming, in which the students design, implement, test and debug programs for computational problems drawn from various fields using Python programming language, while working individually and in groups. This course emphasizes program design process, object-oriented software development approach, and development of practical programming skills that translate to programming in other modern languages. This is a stand-alone introductory computer science course that is not a part of the coordinate major in computer science program. It is aimed at students with no prior computing background who wish to learn the foundations of programming and computational problem solving.

Computational tools are a critical part of our everyday lives. Software is the driving force behind cutting-edge scientific discovery, blockbuster entertainment, and today's fast-paced marketplace. This course is an introduction to techniques, ideas, and problem-solving approaches that are used to develop some of these tools. At a high level, we focus on developing "computational thinking", which is the practice of using abstraction to design and implement algorithms and software to solve problems that arise in many different areas of our daily lives, such as networks, social media, and scientific computing, to name just a few. At a practical level, students will design, implement, test and document their programs to learn introductory programming concepts, such as: data types and data structures (e.g. lists, dictionaries, trees); programming techniques (modular design using functions, recursion, object-oriented programming); performance analysis via theoretical estimate, profiling and timing. Most assignments in this course are programming assignments aimed to teach the students to express their ideas in efficient and elegant code; no prior programming experience is necessary to join and succeed in the course. Lecture periods are dedicated to introducing new material, discussions, individual and group activities. Lab periods are used for programming practice. CMPS 1500 is the first course for the Coordinate Major in Computer Science.

Corequisite lab of CMPS 1500.

This course is an introductory breadth course for computer science, focusing on several core topics in the design, analysis and implementation of computational tools that are drawn from the fields of data structures, software engineering, and programming languages (such as Java, C, Haskell). Object-oriented programming, test-driven development, data structures and abstract data types, imperative programming and memory management, and functional programming will all be covered. By solving practical, real-life problems in different programming languages and in different ways, students learn to select a language and approach most appropriate for the situation and prepare to learn new languages independently. The high-level goal of this course is to train students to be able to draw from a versatile set of skills, which in turn will provide a strong foundation for further study in computer science.

Corequisite lab of CMPS 1600.

Course may be repeated up to unlimited credit hours.

Course may be repeated up to unlimited credit hours.

An introductory course on computer programming and problem-solving using computers. In this course students design, implement, test and debug programs for computational problems using Python programming language. This course emphasizes program design process, object-oriented software development approach, and practical programming skills that translate to programming in other modern languages. Assignments include practical problems drawn from various fields (such as biology, linguistics, graphics, and games).

This course is an introduction to several areas of mathematics that are particularly useful in computer science. The topics include an introduction to predicate and propositional logic, mathematical induction, combinatorics and counting, and discrete probability theory. In lieu of prerequisites please contact instructor for consideration.

Co-requisite lab of CMPS 2170.

This course is an introduction to the design and analysis of algorithms, and covers several basic algorithmic paradigms and their application to core computational problems in graph theory and optimization, as well as analysis of time and space complexity. The primary focus of the course will be on understanding the divide-and-conquer, greedy and dynamic programming paradigms for algorithm design as well as the problem areas to which they can be applied. Example application areas include graph theory, discrete optimization, numeric and scientific computing and machine learning. Topics usually include: asymptotic analysis and big-O notation; divide-and-conquer algorithms; recurrences and the master method; greedy algorithms; graph algorithms (Breadth-First Search, Depth-First Search, Connectivity and Shortest Paths); dynamic programming; linear programming; lower bounds and computational complexity.

Corequisite lab of CMPS 2200.

Modern computer systems must take advantage not only of the latest hardware technology, but also of the ability to compute and communicate over a network. In this course the students will study the principles behind the design of modern operating systems and distributed systems through theoretical study of classic solutions and hands-on programming assignments in C. The study of architecture and organization of modern operating systems focuses on the concepts of virtualization, concurrency, and persistence. In the study of distributed systems we will examine topics such as protocol design, asynchronous and synchronous communication, and layered network architecture.

Corequisite lab of CMPS 2300.

Transfer Coursework at the 2000 level. Department approval may be required.

This course provides an introduction to geometric algorithms and geometric data structures. Computational Geometry is a young discipline which enjoys close relations to mathematics and to various application areas such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems (GIS), VLSI, robotics, computer graphics and geometric modeling. Covered topics include fundamental geometric algorithm design and analysis paradigms, geometric data structures for planar subdivisions and range searching, algorithms to compute the convex hull, Voronoi diagrams, and Delaunay triangulation, as well as selected advanced topics.

The aim of this course is to provide the student with an introduction to the main concepts and techniques playing a key role in the modern arena of artificial intelligence. In addition to covering the main topics that concern modern AI, particular attention will be devoted to its applications in several fields. Among the topics covered are: "What is an intelligent artificial agent?", problem solving using search and constraint satisfaction, uncertainty, Bayesian networks and probabilistic inference, supervised learning, planning, sequential decision problems, as well as several additional topics.

The aim of this course is to provide the student with an introduction to the main concepts and techniques required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SQL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses.

This course covers the fundamentals of 3D game development. We will focus on the Unity game engine and object-oriented programming with C#. You will learn various topics including vector math, event-driven programming, and Unity tools and techniques, the majority of which are applicable to all state-of-the-art game engines.

Over the last few decades, as we have been able to determine whole genome sequences, structural biologists have sought to determine and catalog protein structures with an increasing reliance on computational methods. Automated methods to analyze protein structure make it possible to leverage information from previously solved structures, and to interpret experimental data in a principled way. In this course, we will focus on the myriad of algorithms for analyzing numerous aspects of protein structure and protein-protein interactions.

This course provides an introduction to the fundamental concepts of machine learning and statistical pattern recognition. In addition, several examples of applications will be described. The topics covered include generative/discriminative and parametric/non-parametric supervised learning, including neural networks; unsupervised learning, including clustering, dimensionality reduction and kernel methods; learning theory, including tradeoffs, large margins and VC theory; reinforcement learning, including criteria for optimality, brute force methods, value function methods and direct policy search; feedforward/feedback adaptive control, direct/indirect adaptive control methods; and various applications.

This course is an introduction to the theory of computation. It begins with regular languages and their representation as finite state automata, and continues with context free languages and pushdown automata. Turing machines and the Church-Turing Thesis are also considered, as well as decidability and reducibility. The basic notions of complexity theory area also covered, including P and NP for time complexity, as well as basic results about space complexity.

This course focuses on advanced techniques in the design and analysis of algorithms and illustrates how they are used in deriving a variety of now-classic results. Topics include graph algorithms, randomized algorithms, parallel computing, linear programming, and approximation algorithms.

This course is an introduction to Shannon's mathematical theory of information. It considers basic concepts such as information content, entropy and the Kullback-Leibler distance, as well as areas such as data compression and Shannon's Source Coding Theorem, coding, prefix codes, lossless channels and their capacity, and Shannon's Noisy Coding Theorem. Applications to various areas are also featured in the course.

This is a project-oriented course on fundamentals of software development and software engineering. Working in teams, students apply a recognized software engineering methodology, a modern programming language, and software development tools (including an IDE, debugger, version control system, and testing framework) to design and implement a semester-long project – a software solution for a real-world problem. The high goal of the course is to train students to function efficiently in a real-world software development environment. To help reach that goal, the students do a lot of independent learning, teamwork, documentation and public presentation of their product and design process. The particular technologies employed in the course may change in synchrony with changes in the software engineering field, currently the focus is on engineering software-as-a-service using Ruby for programming language and Rails for web development framework.

This course is an introduction to logic and its applications in computer science. The topics covered include soundness and completeness of propositional logic, predicate logic, linear time temporal logic and branching time temporal logics, and their expressive power, frameworks for software verification, Hoare triples, partial and total correctness, modal logics and agents, and binary decision diagrams.

Introduction to concepts, algorithms, and practices of deep learning. Topics include mathematical building blocks of neural networks, deep learning libraries, stochastic gradient descent, machine learning fundamentals, classification and regression, computer vision, time series analysis, generative deep learning. (CMPS 6340 for graduate credit)

A comprehensive introduction to the mathematics and algorithms that drive today's digital special effects, animation, and games. Designed as a hands-on course, students will gain experience in building 2D/3D interactive applications using OpenGL. Topics covered will include geometric transformations, projections, raster algorithms, 3D object models (surface and volume), visible surface algorithms, texture mapping, lighting/shading, ray-tracing, anti-aliasing, and compositing.

An introduction on how graphical representations of data can be used to aid understanding. This course details the theory and practice of designing effective information or scientific visualizations. The techniques learned in this class have wide applications to all fields in engineering and science, where due to increasing sizes and complexity, data now demands effective presentation and analysis. Topics will include iso-surfacing, volume rendering, transfer functions, vector/tensor fields, topological analysis, large data visualization, and uncertainty in visualizations.

In this course, we will cover the fundamentals of processing digital imagery. Planned topics will include filtering, restoration, enhancement, segmentation, compression, features, and patterns. This will be a practical course with a combination of lectures covering the theory behind processing images and practical programming assignments.

Structure and organization of computer systems; instruction sets; arithmetic; data path and control design; memory hierarchy.

This course varies from time to time, focusing on topics of interest to the faculty and students. Course may be repeated up to unlimited credit hours.

Special topics in computer science

Special Topics Lab. Course may be repeated up to unlimited credit hours.

Students complete a service activity in the community in conjunction with the content of a three-credit co-requisite course. Course may be repeated up to unlimited credit hours.

Transfer Coursework at the 3000 level. Department approval may be required.

This is the first semester of a two-semester course devoted to the development of the student's capstone project, a required component of the Computer Science coordinate major. Under supervision of a faculty advisor in computer science, students use the tools of computer science to solve a problem from another discipline, usually their primary major area.

This is the second of a two-semester course devoted to the development of the student’s capstone project, a required component of the Computer Science coordinate major. Under supervision of a faculty advisor in computer science, students use the tools of computer science to solve a problem from another discipline, usually their primary major area.

This course has two main goals. The first one is to give a broad overview of the fundamentals of multi-agent systems (MAS). MAS are playing an increasingly important role in Artificial Intelligence as distributed resources push for highly distributed forms of intelligence. The second aim is to provide a more in-depth discussion of selected MAS topics: game theory and voting from a computational point of view. Situated at the nexus between economics and computer science, these research areas provide a perfect example of interdisciplinary cross-fertilization and mutual enrichment and lie at the core of multi-agent systems theory. The course will provide the student with an understanding of how self-interested behavior and coordination can be formally modeled and implemented in societies of artificial agents. Course may be repeated up to unlimited credit hours.

This course studies the mathematics underlying computer security, including both public key and symmetric key cryptography, crypto-protocols and information flow. The course includes a study of the RSA encryption scheme, stream and clock ciphers, digital signatures and authentication. It also considers semantic security and analysis of secure information flow.

Human-Computer Interaction (HCI) is a rapidly expanding field of research and development that continues to transform how we use computers over the past five decades. This course is designed for both graduate and undergraduate students interested in understanding fundamental HCI theories, techniques, and research methods. The course covers various topics, including human factors (perception and cognition) in computing, graphical design fundamentals, qualitative and quantitative user research methods, interaction modeling, prototyping, and evaluations. The course also briefly reviews emerging areas in HCI research, including human-AI interaction, accessible computing, ubiquitous computing, and augmented reality. Both undergraduate and graduate students will work on semester-long projects.

This is a hands-on introductory security course designed for upper-level undergraduate students. The course covers the basics of cryptography, methods for safeguarding computer systems from attacks, and malicious software. Students will also learn about network attacks and perimeter defenses used to prevent them. The course further includes an in-depth study of program vulnerabilities that lead to most of the security issues in computing today. Lastly, the course will cover administrative issues that security professionals must consider in their jobs.

This course covers fundamental algorithm design principles and data structures, basic notions of complexity theory, as well as an advanced introduction to parallel algorithms, randomized algorithms, and approximation algorithms. Topics include: divide-and-conquer, dynamic programming, amortized analysis, graph algorithms, network flow, map reduce, and more advanced topics in approximation algorithms and randomized algorithms.

This course is designed for graduate students interested in understanding the design of autonomous intelligent agents. The course will cover fundamental notions and concepts such as uninformed and informed search, local search, constraint satisfaction and constraint-based optimization, Bayesian Networks, Markov Decision Problems and a short introduction on machine learning. Furthermore, advance topics and applications in the context of natural language processing, reasoning about time, algorithmic game theory and computational social choice will be considered as well.

This course is an introduction to computational methods in molecular biology. Topics covered include: sequence analysis and alignment, sequencing technologies, genome and metagenomic sequencing, protein structure and structure prediction, and phylogenetic analysis. No prior background in biology is assumed.

This course focuses on advanced principles for designing and analyzing geometric algorithms and data structures, and their application to other disciplines. Selected topics may include: Dynamic and kinetic data structures, geometric algorithms and data structures in two and higher dimensions, shape analysis and matching, robustness and implementation issues, geometric approximation algorithms. Prerequisites: Introduction to Algorithms or equivalent, or permission by the instructor. CMPS 3130/6130 preferred.

Special topics in Computer Science. Course may be repeated up to unlimited credit hours.

Special topics in computer science

This course is an advanced introduction to the area of computational complexity. Topics covered include: impossibility and separability results for classical computation, interactive theorem proving and the PCP theorem, derandomization and hardness of approximation, and the quantum model of computation.

This course will cover fundamental and advanced topics in machine learning. Topics will include linear and logistic regression, Lasso, preceptrons, deep neural networks, support vector machines, kernel methods, graphical models, principal and independent component analysis and Gaussian processes. In addition to thoroughly addressing theoretical aspects, several examples will illustrate the application of the different techniques.

This course investigates computational methods to work with human language, analyzing its lexical, syntactic, and semantic aspects. Examples include document classification and clustering, syntactic parsing, information extraction, speech recognition, and machine translation. Theoretical and practical aspects of the latest techniques will be covered, including probabilistic modeling, neural networks, and deep learning.

Reinforcement learning (RL) has found successful applications in various domains, including recommender systems, health care, energy, finance, robotics, transportation, and computer systems. Many people believe that RL is a step toward Artificial General Intelligence (AGI). This course introduces both the classic results and state-of-the-art research in RL at the graduate level. We will cover both the theoretical foundation of RL and its applications through case studies.

The objective of the course is to introduce students to the core concepts and analytic techniques in the design and analysis of computer networks and network protocols. We will explain both how computer networks work using the Internet as the paradigm and why they work from an optimization and control perspective.

This course covers the fundamental concepts in distributed computing. The objective is to introduce students to the core notions, algorithms, and analytic tools in the design of distributed systems. Recent developments in peer-to-peer systems, cloud computing, sensor networks, etc. will be used as case studies to help students establish a firm understanding of the philosophy and pitfalls in the design of computer systems when there is no global clock and when unpredictable failures and variable latency are the norm.

This course teaches students fundamental knowledge in Computer Architecture and Microarchitecture. The objective is to train students in the concepts and methodology of design and the evaluation of computer processors. The topics include fundamentals of computer design, instruction-level parallelism, memory hierarchy, multiprocessors and thread-level parallelism, and recent advances in this area.

This course is designed for both graduate students and advanced undergraduate students interested in understanding of both the fundamental and advanced concepts, techniques, and technologies required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SQL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses. Students should be comfortable programming in Python and familiar with the fundamentals of algorithmic analysis and computer systems.

This course provides a comprehensive introduction to computer vision techniques and applications. Major topics include image processing, image segmentation (semantic segmentation, co-segmentation), image enhancement (super-resolution, image recovery), 3D reconstruction, image generation, and video segmentation. It will also touch several advanced computer vision topics, such as multi-modal visual understanding, transfer learning, style transfer and image translation. Students will learn the basic concepts of computer vision as well as gain hands-on experience in solving real-life vision problems

Students complete a service activity in the community in conjunction with the content of a three-credit corequisite course.

Service Learning Internship

This is a directed study course that allows a student to pursue a topic of particular interest under the direction of a computer science faculty member. No more than three hours of 4910-4920 may be counted toward satisfying the major requirements. Course may be repeated up to unlimited credit hours.

This is a directed study course that allows a student to pursue a topic of particular interest under the direction of a computer science faculty member. No more than three hours of 4910-4920 may be counted toward satisfying the major requirements.

Transfer coursework at the 4000 level. Departmental approval required.

For especially qualified seniors with approval of the faculty director and the Office of Academic Enrichment. Students must have a minimum of a 3.400 overall grade-point average and a 3.500 grade-point average in the major.

Courses taught abroad by non-Tulane faculty. Does not count toward Tulane GPA. Course may be repeated up to unlimited credit hours.

Courses taught abroad by non-Tulane faculty. Does not count toward Tulane GPA. Course may be repeated up to unlimited credit hours.

This course is a graduate level introduction to computer science. Not assuming a background in computer science, it introduces programming fundamentals such as modular design, recursion, and object-oriented programming, key ideas from algorithms and analysis including algorithm design, computational complexity, and parallelism, fundamental data structures, crucial concepts from operating systems, and the organization and design of computer networks. Students in this course will establish a broad foundation for the future study and exploration of computer science. This is a stand-along graduate introductory computer science course that does not count towards any graduate program in computer science.

This course provides an introduction to geometric algorithms and geometric data structures. Computational Geometry is a young discipline which enjoys close relations to mathematics and to various application areas such as geometric databases, molecular biology, sensor networks, visualization, geographic information systems (GIS), VLSI, robotics, computer graphics and geometric modeling. Covered topics include fundamental geometric algorithm design and analysis paradigms, geometric data structures for planar subdivisions and range searching, algorithms to computer the convex hull, Voronoi diagrams, and Delaunay triangulation, as well as selected advanced topics.

The aim of this course is to provide the student with an introduction to the main concepts and techniques playing a key role in the modern arena of artificial intelligence. In addition to covering the main topics that concern modern AI, particular attention will be devoted to its applications in several fields. Among the topics covered are: "What is an intelligent artificial agent?", problem solving using search and constraint satisfaction, uncertainty, Bayesian networks and probabilistic inference, supervised learning, planning, sequential decision problems, as well as several additional topics.

This course has two main goals. The first one is to give a broad overview of the fundamentals of multi-agent systems (MAS). MAS are playing an increasingly important role in Artificial Intelligence as distributed resources push for highly distributed forms of intelligence. The second aim is to provide a more in depth discussion of selected MAS topics: game theory and voting from a computational point of view. Situated at the nexus between economics and computer science, these research areas provide a perfect example of interdisciplinary cross-fertilization and mutual enrichment and lie at the core of multi-agent systems theory. The course will provide the student with an understanding of how self-interested behavior and coordination can be formally modeled and implemented in societies of artificial agents. Course may be repeated up to unlimited credit hours.

The aim of this course is to provide the student with an introduction to the main concepts and techniques required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SOL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses.

This course covers the fundamentals of 3D game development. We will focus on the Unity game engine and object-oriented programming with C#. You will learn various topics including vector math, event-driven programming, and Unity tools and techniques, the majority of which are applicable to all state-of-the-art game engines.

This course focuses on 3D game development using Unity game engine and object-oriented programming with C#. You will learn various topics including vector math, event-driven programming, and Unity tools and techniques, the majority of which are applicable to all state-of-the-art game engines, as well as several more advanced topics in game development.

Over the last few decades, as we have been able to determine whole genome sequences, structural biologists have sought to determine and catalog protein structures with an increasing reliance on computational methods. Automated methods to analyze protein structure make it possible to leverage information from previously solved structures, and to interpret experimental data in a principled way. In this course, we will focus on the myriad of algorithms for analyzing numerous aspects of protein structure and protein-protein interactions.

This course provides an introduction to the fundamental concepts of machine learning and statistical pattern recognition. In addition, several examples of applications will be described. The topics covered include generative/discriminative and parametric/non-parametric supervised learning, including neural networks; unsupervised learning, including clustering, dimensionality reduction and kernel methods; learning theory, including tradeoffs, large margins and VC theory; reinforcement learning, including criteria for optimality, brute force methods, value function methods and direct policy search; feedforward/feedback adaptive control, direct/indirect adaptive control methods; and various applications.

This course studies the mathematics underlying computer security, including both public key and symmetric key cryptography, crypto-protocols and information flow. The course includes a study of the RSA encryption scheme, stream and clock ciphers, digital signatures and authentication. It also considers semantic security and analysis of secure information flow.

This course focuses on advanced techniques in the design and analysis of algorithms and illustrates how they are used in deriving a variety of now-classic results. Topics include graph algorithms, randomized algorithms, parallel computing, linear programming and approximation algorithms.

This course is an introduction to Shannon's mathematical theory of information. It considers basic concepts such as information content, entropy and the Kullback-Leibler distance, as well as areas such as data compression and Shannon's Source Coding Theorem, coding, prefix codes, lossless channels and their capacity, and Shannon's Noisy Coding Theorem. Applications to various areas are also featured in the course.

This is a project-oriented course on fundamentals of software development and software engineering. Working in teams, students apply a recognized software engineering methodology, a modern programming language, and software development tools (including an IDE, debugger, version control system, and testing framework) to design and implement a semester-long project – a software solution for a real-world problem. The high goal of the course is to train students to function efficiently in a real-world software development environment. To help reach that goal, the students do a lot of independent learning, teamwork, documentation and public presentation of their product and design process. The particular technologies employed in the course may change in synchrony with changes in the software engineering field, currently the focus is on engineering software-as-a-service using Ruby for programming language and Rails for web development framework.

This course is an introduction to logic and its applications in computer science. The topics covered include soundness and completeness of propositional logic, predicate logic, linear time temporal logic and branching time temporal logics and their expressive power, frameworks for software verification, Hoare triples, partial and total correctness, modal logics and agents, and binary decision diagrams.

Human-Computer Interaction (HCI) is a rapidly expanding field of research and development that continues to transform how we use computers over the past five decades. This course is designed for both graduate and undergraduate students interested in understanding fundamental HCI theories, techniques, and research methods. The course covers various topics, including human factors (perception and cognition) in computing, graphical design fundamentals, qualitative and quantitative user research methods, interaction modeling, prototyping, and evaluations. The course also briefly reviews emerging areas in HCI research, including human-AI interaction, accessible computing, ubiquitous computing, and augmented reality. Graduate students will work on semester-long independent projects.

Introduction to concepts, algorithms, and practices of deep learning. Topics include mathematical building blocks of neural networks, deep learning libraries, stochastic gradient descent, machine learning fundamentals, classification and regression, computer vision, time series analysis, generative deep learning. (Undergraduate-level course is CMPS 3340.)

A comprehensive introduction to the mathematics and algorithms that drive today's digital special effects, animation, and games. Designed as a hands-on course, students will gain experience in building 2D/3D interactive applications using OpenGL. Topics covered will include geometric transformations, projections, raster algorithms, 3D object models (surface and volume), visible surface algorithms, texture mapping, lighting/shading, ray-tracing, anti-aliasing, and compositing.

An introduction on how graphical representations of data can be used to aid understanding. This course details the theory and practice of designing effective information or scientific visualizations. The techniques learned in this class have wide applications to all fields in engineering and science, where due to increasing sizes and complexity, data now demands effective presentation and analysis. Topics will include iso-surfacing, volume rendering, transfer functions, vector/tensor fields, topological analysis, large data visualization, and uncertainty in visualizations.

This is a hands-on introductory course on Software Security. This course covers several key topics critical to understanding software security and analyzing malicious software. These topics include cryptography, software security analysis techniques, program vulnerabilities, binary disassembly, fuzzing, and malicious Windows programs. This course will also dive into malware functionality and behavior on computers and over the network to include techniques used to evade detection.

Structure and organization of computer systems; instruction sets; arithmetic; data path and control design; memory hierarchy.

This course covers fundamental algorithm design principles and data structures, basic notions of complexity theory, as well as an advanced introduction to parallel algorithms, randomized algorithms, and approximation algorithms. Topics include: divide-and-conquer, dynamic programming, amortized analysis, graph algorithms, network flow, map reduce, and more advanced topics in approximation algorithms and randomized algorithms.

This course is designed for graduate students interested in understanding the design of autonomous intelligent agents. The course will cover fundamental notions and concepts such as uninformed and informed search, local search, constraint satisfaction and constraint-based optimization, Bayesian Networks, Markov Decision Problems and a short introduction on machine learning. Furthermore, advance topics and applications in the context of natural language processing, reasoning about time, algorithmic game theory and computational social choice will be considered as well.

This course is an introduction to computational methods in molecular biology. Topics covered include: sequence analysis and alignment, sequencing technologies, genome and metagenomic sequencing, protein structure and structure prediction, and phylogenetic analysis. No prior background in biology is assumed.

This course focuses on advanced principles for designing and analyzing geometric algorithms and data structures, and their application to other disciplines. Selected topics may include: Dynamic and kinetic data structures, geometric algorithms and data structures in two and higher dimensions, shape analysis and matching, robustness and implementation issues, geometric approximation algorithms. CMPS 3130/6130 preferred.

This course varies from time to time, focusing on topics of interest to the faculty and students. Course may be repeated up to unlimited credit hours.

Special topics in computer science

This course is an advanced introduction to the area of computational complexity. Topics covered include: impossibility and separability results for classical computation, interactive theorem proving and the PCP theorem, derandomization and hardness of approximation, and the quantum model of computation.

This course will cover fundamental and advanced topics in machine learning. Topics will include linear and logistic regression, Lasso, preceptrons, deep neural networks, support vector machines, kernel methods, graphical models, principal and independent component analysis and Gaussian processes. In addition to thoroughly addressing theoretical aspects, several examples will illustrate the application of the different techniques.

This course investigates computational methods to work with human language, analyzing its lexical, syntactic, and semantic aspects. Examples include document classification and clustering, syntactic parsing, information extraction, speech recognition, and machine translation. Theoretical and practical aspects of the latest techniques will be covered, including probabilistic modeling, neural networks, and deep learning.

Reinforcement learning (RL) has found successful applications in various domains, including recommender systems, health care, energy, finance, robotics, transportation, and computer systems. Many people believe that RL is a step toward Artificial General Intelligence (AGI). This course introduces both the classic results and state-of-the-art research in RL at the graduate level. We will cover both the theoretical foundation of RL and its applications through case studies.

The objective of the course is to introduce students to the core concepts and analytic techniques in the design and analysis of computer networks and network protocols. We will explain both how computer networks work using the Internet as the paradigm and why they work from an optimization and control perspective.

This course covers the fundamental concepts in distributed computing. The objective is to introduce students to the core notions, algorithms, and analytic tools in the design of distributed systems. Recent developments in peer-to-peer systems, cloud computing, sensor networks, etc. will be used as case studies to help students establish a firm understanding of the philosophy and pitfalls in the design of computer systems when there is no global clock and when unpredictable failures and variable latency are the norm.

This course covers the design and implementation of operating systems. Operating systems serve as an interface between the hardware of a computer and the software running on it. This course addresses how operating systems enable the sharing of limited resources - CPU, memory, hard disks - robustly, securely, and efficiently amongst all running processes on a computer. CPU virtualization, concurrency, memory virtualization, file systems APIs and implementations, and security are all covered with a focus on the key design ideas and abstractions within the topics. In addition, students get practical experience implementing their own operating system.

This course teaches students fundamental knowledge in Computer Architecture and Microarchitecture. The objective is to train students on the concepts and methodology of the design and evaluation of computer processors. The topics include fundamentals of computer design, instruction-level parallelism, memory hierarchy, multiprocessors and thread-level parallelism, and recent advances in this area. It is recommended that students have taken CMPS 3510/6510, or have equivalent experience before taking this course.

This course is designed for both graduate students and advanced undergraduate students interested in understanding of both the fundamental and advanced concepts, techniques, and technologies required for collecting, processing, and deriving insight into data. Data Science is an interdisciplinary set of topics that includes everything you need to create data driven answers and solutions to specific business, scientific, or sociological questions. Topics typically covered include an introduction to one or more data collection and management systems, e.g., SQL, web scraping, and various data repositories; exploratory and statistical data analysis, e.g., bootstrapping, measures of central tendency, hypothesis testing and machine learning techniques including linear regression and clustering; data and information visualization, e.g., plotting and interactive charts using various technologies; and presentation and communication of the results of these analyses. Students should be comfortable programming in Python and familiar with the fundamentals of algorithmic analysis and computer systems.

This course provides a comprehensive introduction to computer vision techniques and applications. Major topics include image processing, image segmentation (semantic segmentation, co-segmentation), image enhancement (super-resolution, image recovery), 3D reconstruction, image generation, and video segmentation. It will also touch several advanced computer vision topics, such as multi-modal visual understanding, transfer learning, style transfer and image translation. Students will learn the basic concepts of computer vision as well as gain hands-on experience in solving real-life vision problems

This is a directed study course that allows a student to pursue a topic of particular interest under the direction of a computer science faculty member.

Transfer coursework at the 6000 level. Departmental approval required.

This seminar course introduces students to research methods in Computer Science and to the research conducted in the department. Students will read research papers, participate in active research projects, and practice preparing and presenting research presentations. Department faculty will present on their research in order to expose students to the research projects conducted in the department. Research methods such as literature search, experiment design, technical writing, etc. will also be covered. This course is required for all PhD students in Computer Science. The content of this course varies from semester to semester.

In this course PhD students engage in a research project in Computer Science, under the direction of a faculty member, normally the student's faculty advisor. At the beginning of the course the scope of the project will be determined by a project proposal prepared by the student. Research will be conducted on an individual basis with the faculty advisor. The student will prepare a final report that summarize the research methodologies and the research outcomes. The content of this course varies from semester to semester.

In this course PhD students engage in a research project in Computer Science, under the direction of a faculty member, normally the student's faculty advisor. At the beginning of the course the scope of the project will be determined by a project proposal prepared by the student. Research will be conducted on an individual basis with the faculty advisor. The student will prepare a final report that summarize the research methodologies and the research outcomes. The content of this course varies from semester to semester.

In this course PhD students engage in a research project in Computer Science, under the direction of a faculty member, normally the student's faculty advisor. At the beginning of the course the scope of the project will be determined by a project proposal prepared by the student. Research will be conducted on an individual basis with the faculty advisor. The student will prepare a final report that summarize the research methodologies and the research outcomes. The content of this course varies from semester to semester.

This course varies from time to time, focusing on advanced topics of interest to the faculty and students.

Transfer coursework at the 7000 level. Departmental approval required.

This is a directed study course that allows a graduate student to pursue a topic of particular interest under the direction of a computer science faculty member. Course may be repeated up to unlimited credit hours.

Research toward completion of a masters degree. Course may be repeated up to unlimited credit hours.

Research toward completion of a doctoral degree. Course may be repeated up to unlimited credit hours.