23 courses with the subject CMPG, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
CMPG 612Operating Systems3
This course focuses on the issues in the design and functioning of operating systems. Topics include file systems, CPU scheduling, memory management, virtual memory and machines, disk scheduling, deadlocks and their prevention, concurrency, protection mechanisms, multiprocessors, distributed systems and security. The course will include a case study of the Linux kernel code, along with other modern operating systems, illustrating the various topics.
This course focuses on the design and analysis of efficient algorithms. Topics include advanced data structures and sorting algorithms. Algorithm design techniques such as dynamic programming, greedy algorithms, amortized analysis will be discussed. Algorithms for graph problems such as minimum-cost spanning tree, shortest paths and maximum flow will also be discussed.
This course focuses on the foundations of database systems and SQL programming. Topics such as the relational algebra and data model, schema normalization, query optimization, indexing and transaction processing will be discussed. Students will use MySQL for hands-on experimentation with writing queries.
This is a graduate level course in computer networks. This course focus on studying the state of the art in networking and networked systems. Topics to be covered include TCP/IP, internet routing, peer-to-peer systems, congestion management, QoS, network management, wireless communication and network security. Each topic will provide a background on traditional perspectives and provide an update on current and ongoing research. Students will learn concepts, techniques and tools while learning to carry out original research in course projects. In addition, students will gain experience in reading technical papers, giving conference-style presentation and writing project report.
This course will be a survey of the field of Artificial Intelligence. Topics include intelligent agents, informed and uninformed search, game trees and constraint satisfaction problems. Selected machine learning topics, such as decision trees and Bayesian network will also be discussed. Cross-listed with CMPT-420 Artificial Intelligence.
This course provides an overview of Python programming and covers the fundamentals, the object-oriented features, and design of applications. The emphasis will be on the logical analysis of a problem and the formulation of a computer program leading to its solution using Python. Students will be required to work on a variety of programming assignments in Python. Topics include but are not limited to class design, inheritance, polymorphism, design of applications, and AI-related applications in particular. Cross-listed with CMPT-342 Python Programming.
A study of the principles and methods advocated for the development of large and complex software systems. Each student will be required to participate in a team project devoted to the specification, design and implementation of a sizable software system. Cross-listed with CMPT 456 Software Engineering.
CMPG 758Blockchain and Cryptocurrency Technologies3
This course provides a comprehensive introduction to the revolutionary blockchain and cryptocurrency technologies. This course covers topics related to the new global money for the Internet age. This course explain how blockchain technology is transforming the Internet, allow students to understand bitcoin, cryptocurrencies and how they are disrupting the financial industry, have a comprehensive understanding of where blockchain technology is headed and how it can be leveraged. Cross-listed with CMPT 368.
This course focuses on fundamental data mining algorithms and their applications in the process of knowledge discovery. The course will cover the general aspects and techniques of analyzing large, complex datasets, recognizing patterns and making predictions. The R programming language will also be introduced and used for hands-on experimentation with data mining algorithms. Cross-listed with CMPT-463 Topics in Computer Science: Data Mining.
This course offers an in-depth study of Cloud Computing and its underlying technologies, specifically Virtualization. Areas of discussion include the internal architecture of clouds, the architecture and structure of Virtual Machines, and cloud management, security, and optimizations. The course also covers Linux Containers and their features. The course supplements all the topics with tracing actual software code (Xen, KVM, QEMU, VirtualBox), study of the latest related research publications, and hands-on experience with the relevant technologies (AWS, Live Migration, Nested Virtualization). Cross-listed with CMPT-464 Topics in Computer Science: Cloud Computing and Virtualization.
This course provides the basic concepts of neural networks and other learning techniques including but not limited to: biological foundations of neural networks, basics of neural information processing, an artificial neuron and its activation function, multilayer feedforward neural networks and backpropagation learning, deep learning, Hopfield neural networks and associative memories, recurrent neural networks, support vector machines, validation of learning results, and clustering. Laboratory exercises provide experience with design and utilization neural and other machine learning algorithms and solving real-world classification, prediction, pattern recognition and intelligent data analysis problems. A course project will help students to develop their team-working skills and get a good experience in software project design. Cross-listed with CMPT-465 Neural Networks and Learning Systems.
This course provides the basic concepts of image processing and analysis including but not limited to image sensing and acquisition, visual perception, image enhancement (mostly spatial domain image enhancement, but some essential elements of the frequency domain enhancement will also be considered), image filtering in spatial and frequency domain, edge detection and image segmentation, elements of image restoration, image understanding and recognition, elements of color image processing. Laboratory exercises provide experience with design and software utilization of image processing algorithms and processing images related to various real-world applications (medical and satellite image processing, old images restoration, and digital photography). Students will program various algorithms and use their programs for processing real images. This will help them to accomplish specified challenges as they develop problem solving skills. A course project will help students to develop their team-working skills and get a good experience of software project design. Cross-listed with CMPT 477.
This course provides a basic introduction to the principles and practice of cryptography and computer security. Topics include perfect secrecy, block ciphers, public key cryptosystems, key management, certificates, public key infrastructure (PKI), hash functions, digital signatures, non-repudiation, message authentication, access control, email and web security, intrusion detection, firewalls and security policies. Various security standards and protocols such as DES, AES, PGP, and SSL are also discussed. Cross-listed with CMPT 448- Cryptography and Security.
In this course, students will learn computer and network security fundamentals by studying attacks on computer systems, networks, and the Web. Students will learn how these attacks work and how to detect and prevent the attacks. The course takes a hands-on approach by explaining theories via specially designed labs. Students are required to conduct a series of experimental exercises. Through these experiments, this course will help students enhance their understanding of principles and use these principles to solve practical problems. Cross-listed with CMPT-369 Cyber Security Lab.
This course provides a comprehensive overview of Web security. The goal is to understand the most common web attacks and their countermeasures. We'll cover the fundamentals as well as the state-of-the-art in Web security. Topics include Principles of web security, attacks and countermeasures, denial-of-service, same-origin policy, cross site scripting, authentication, the web app vulnerabilities, injection, TLS attacks, privacy, etc. Course components include lectures, hands-on labs, 2-3 in-class quizzes, and one group course project. Cross-listed with CMPT-370 Web Security.
This course introduces the principles and practice of parallel computing, with a focus on modern applications in artificial intelligence. Students will learn models of parallel computation, parallel architectures, and programming techniques such as message passing, shared memory, and GPU computing. The course emphasizes design, analysis, and implementation of parallel algorithms, highlighting their role in accelerating AI and machine learning tasks. Topics include parallel sorting and searching, matrix operations, and neural network training. Case studies will demonstrate how parallel computing enables large-scale deep learning, and other AI-related applications. By the end of the course, students will be able to analyze problems for parallelism, implement parallel solutions, and evaluate performance trade-offs. Cross-listed with CMPT-471 Parallel Computing.
CMPG 778Coding Interview Preparation: Algorithms, Data Structures and Skills3
This elective course will help students prepare for the unique aspects of a coding job interview, with programming techniques, computer science foundations, strategic insights, practicing skills and some tips. Class meeting will consist of lectures and programming activities. Students will work on a lot of coding exercises to learn problem-solving techniques, improve the understanding of data structures and algorithms. The LeetCode platform will be used throughout the course. In addition, there will be an in-class programming competition and a mock interview session to allow students to get some practice in real-life simulated environments. Cross-listed with CMPT 478.
This course focuses on the Linux Kernel, a large-scale open source software project. Topics include in-depth discussions, and hands-on modifications of the Linux memory, process, storage, and network sub-systems. Programming topics include creating kernel modules, simple device drivers, as well as modifying and compiling the kernel source code. Cross-listed with CMPT 480.
This course introduces the fundamentals of deep learning and generative artificial intelligence (AI), focusing on both theoretical understanding and practical implementation. Students will learn core deep learning concepts, deep neural networks architectures, convolutional neural networks, image recognition using convolutional neural networks, generative AI neural network architectures, and state-of-the-art generative AI models. Applications of generative AI in text and image generation will be considered. The course emphasizes hands-on work, with biweekly projects that build towards a final course project. Cross-listed with CMPT-485.
This course may cover any advanced topic in modern Computer Science. It is offered as an elective when there is a demand. The instructor's permission is required to take this course.
This course requires from a student to develop an independent research project under supervision of and in collaboration with an instructor. It should typically be resulted in a conference or journal paper submitted or prepared for submission (or in a detailed written report) followed by an oral presentation in the department. This course should typically be taken by "visiting" or exchange graduate students from other institutions doing joint research with one of the Computer Science faculty. A research project shall be approved by the department graduate committee. It can also be taken as a graduate elective by graduate students not taking CMPG-798 and CMPG-799, if they have a research project to work on with one of the Computer Science faculty and this project was approved by the department graduate committee. A permission from the Department Chair is required.
The aim of the Master Thesis/Master Project in the graduate Computer Science program is to help students to strengthen their knowledge and skills, put ideas and concepts to work in solving actual problems and finally become successful professionals able to gain employment in industry and/or to be accepted into a Computer Science Ph.D. program. Students elected for Master Thesis should work on a master level research project mentored by a faculty member. Students elected for Master Project should develop a sophisticated software system for solving a real-world computational problem as practiced in industry. The work can be performed as a team work (Project) or can be performed as an individual project design or research (Thesis). This course (Master Thesis/Master Project Seminar) is the first course in a 2-semester course sequence. It requires students to develop a research or software design project proposal based on the knowledge and skills acquired in earlier coursework. The research and design concepts should include a detailed feasibility study as well as economical, societal, environmental and ethical aspects. At the end of the semester the design group or individual makes a proposal presentation and submits a detailed project proposal.
This course (Master Thesis/Master Project) is the second course in a 2-semester course sequence. It requires students to develop a research or software design project based on the knowledge and skills acquired in earlier coursework. This course covers the second phase of the Master Thesis research or Master Project design. In this course, students perform and complete actual design and testing of the software system proposed at the first phase (CMPT-798). At the end of the semester an individual working on a Master Thesis submits the thesis and makes a formal final presentation of the obtained results. Respectively, at the end of the semester each design group or a sole designer working on a Mater Project makes a formal final presentation, demonstrates the software system designed, and submits a final report clearly documenting all aspects of the design process. Final presentations should be attended by interested students, guests, faculty members, engineers and IT professionals from local industries. Prerequisite CMPT-798 with the grade not lower than B.
