diff --git a/content/graduate/courses/cmpe510/_index.md b/content/graduate/courses/cmpe510/_index.md new file mode 100644 index 00000000..bb74e945 --- /dev/null +++ b/content/graduate/courses/cmpe510/_index.md @@ -0,0 +1,49 @@ +--- +title: CMPE510 +description: Machine Translation +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe510/1400 +aliases: + - graduate/courses/cmpe510 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE510 | +| Course Title | Machine Translation | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Machine translation terminology. Rule-based machine translation approaches: direct, transfer and interlingua. The Vauquois triangle. Statistical machine translation. Word-based translation and phrase-based models. Word alignment. Decoding. Language models in translation. Example-based machine translation. Quality assessment of machine translation. + +## Course Learning Outcomes + +- Understand foundational machine translation terminology and concepts +- Become familiar with rule-based approaches (direct, transfer, interlingua) +- Learn the Vauquois triangle as a conceptual framework for translation processes +- Gain knowledge of statistical machine translation and phrase-based models +- Understand word alignment, decoding, and language modeling in MT +- Learn example-based MT and methods for quality assessment + +## Current Instructor + +{{< people tag="cmpe510" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe510" cols="3">}} diff --git a/content/graduate/courses/cmpe511/_index.md b/content/graduate/courses/cmpe511/_index.md new file mode 100644 index 00000000..69789a6f --- /dev/null +++ b/content/graduate/courses/cmpe511/_index.md @@ -0,0 +1,49 @@ +--- +title: CMPE511 +description: Computer Architecture +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe511/1400 +aliases: + - graduate/courses/cmpe511 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE511 | +| Course Title | Computer Architecture | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | CMPE 344 | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Pushing a single processor to its limits. Instruction set design and its effect on computer performance. Microprogramming. Addressing techniques. Memory hierarchy. Associative, virtual and cache memory. Memory management. Interrupts, DMA and channels. Comparative study of commercial computer architecture. + +## Course Learning Outcomes + +- Understand classical and modern computer architecture concepts +- Analyze the impact of instruction set design on performance +- Learn microprogramming and advanced addressing techniques +- Gain knowledge of memory hierarchy and cache/virtual memory systems +- Understand interrupts, DMA, and channel-based I/O mechanisms +- Compare architectural decisions used in commercial computer systems + +## Current Instructor + +{{< people tag="cmpe511" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe511" cols="3">}} diff --git a/content/graduate/courses/cmpe516/_index.md b/content/graduate/courses/cmpe516/_index.md new file mode 100644 index 00000000..c47ce6d8 --- /dev/null +++ b/content/graduate/courses/cmpe516/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE516 +description: Fault Tolerant Computing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe516/1400 +aliases: + - graduate/courses/cmpe516 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE516 | +| Course Title | Fault Tolerant Computing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | MATH 343 or equivalent, CMPE 511 | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Fault modeling. Test generation for combinational and sequential circuits. Testing of microprocessor based systems. Design for testability. Redundancy techniques to achieve fault-tolerance. Reliability modeling and analysis. Software testing strategies. Software reliability achievement. + +## Course Learning Outcomes + +- Learn fundamental fault models and their usage +- Understand testing methods for combinational and sequential circuits +- Analyze testing approaches for microprocessor-based systems +- Apply design-for-testability concepts +- Learn redundancy techniques for fault-tolerance +- Understand reliability modeling & analysis +- Gain familiarity with software testing strategies and software reliability + +## Current Instructor + +{{< people tag="cmpe516" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe516" cols="3">}} diff --git a/content/graduate/courses/cmpe521/_index.md b/content/graduate/courses/cmpe521/_index.md new file mode 100644 index 00000000..200d0399 --- /dev/null +++ b/content/graduate/courses/cmpe521/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE521 +description: Principles of Database Systems +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe521/1400 +aliases: + - graduate/courses/cmpe521 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE521 | +| Course Title | Principles of Database Systems | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | CMPE 321 | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Fundamental concepts of data modeling and popular data models. Design theory for relational databases. Query optimization and data manipulation languages. Concurrency and protection. + +## Course Learning Outcomes + +- Understand fundamental concepts of data modeling and popular data models +- Learn design theory for relational databases +- Gain familiarity with query optimization techniques +- Understand data manipulation languages in database systems +- Learn basic concepts of concurrency control and protection + +## Current Instructor + +{{< people tag="cmpe521" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe521" cols="3">}} diff --git a/content/graduate/courses/cmpe523/_index.md b/content/graduate/courses/cmpe523/_index.md new file mode 100644 index 00000000..4871d4c7 --- /dev/null +++ b/content/graduate/courses/cmpe523/_index.md @@ -0,0 +1,49 @@ +--- +title: CMPE523 +description: Performance Evaluation of Computer Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe523/1400 +aliases: + - graduate/courses/cmpe523 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE523 | +| Course Title | Performance Evaluation of Computer Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Introduction for computer networks performance evaluation. Modeling of traffic flows. Delay and loss models for computer networks. Networks of queues. Performance evaluation of multiple access methods and local area networks. Measurement and simulation of computer networks. + +## Course Learning Outcomes + +- Understand the fundamentals of performance evaluation in computer networks +- Learn modeling techniques for traffic flows +- Analyze delay and loss models in various network settings +- Understand networks of queues and their applications +- Evaluate performance of multiple access methods and LANs +- Gain experience with measurement and simulation of computer networks + +## Current Instructor + +{{< people tag="cmpe523" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe523" cols="3">}} diff --git a/content/graduate/courses/cmpe524/_index.md b/content/graduate/courses/cmpe524/_index.md new file mode 100644 index 00000000..e191c06d --- /dev/null +++ b/content/graduate/courses/cmpe524/_index.md @@ -0,0 +1,44 @@ +--- +title: CMPE524 +description: Computer Network Design +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe524/1400 +aliases: + - graduate/courses/cmpe524 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE524 | +| Course Title | Computer Network Design | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Principles of computer network design. Network design and optimization algorithms. Centralized network design, switching node location problems. Application of minimum spanning tree and shortest path algorithms to problems in network design. Static and dynamic routing algorithms. Network reliability analysis in design. Adhoc and cellular wireless network design. Case studies. + +## Course Learning Outcomes + +- Understand the fundamental principles of computer network design +- Learn optimization algorithms used in network design +- Analyze switching node placement and centralized design problems +- Apply MST and shortest-path algorithms to design scenarios +- Understand static and dynamic routing algorithms +- Gain familiarity with network reliability analysis +- Learn design methodologies for adhoc and cellular wireless networks +- Study real-world network design case examples + diff --git a/content/graduate/courses/cmpe526/_index.md b/content/graduate/courses/cmpe526/_index.md new file mode 100644 index 00000000..1c8d0c5a --- /dev/null +++ b/content/graduate/courses/cmpe526/_index.md @@ -0,0 +1,52 @@ +--- +title: CMPE526 +description: Operating System and Network Security +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe526/1400 +aliases: + - graduate/courses/cmpe526 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE526 | +| Course Title | Operating System and Network Security | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Operating system and computer network security basics, risk analysis, security policies, concept of trusted computers and networks. Conventional and public key cryptography. Authentication and digital signatures. Authentication protocols and applications, Kerberos, certificates. UNIX/LINUX security issues. IP, TCP/UDP, SSL, DNS, FTP/TELNET, NIS/NFS, SNMP, electronic mail and web security. Firewalls and security tools. Secure payment systems. Case studies and programming projects. + +## Course Learning Outcomes + +- Understand basic concepts of operating system and network security +- Learn risk analysis and security policy fundamentals +- Gain familiarity with trusted computers and networks +- Understand conventional and public-key cryptography +- Learn authentication mechanisms, digital signatures, and protocols (e.g., Kerberos, certificates) +- Analyze UNIX/Linux security issues +- Understand security aspects of core internet protocols (IP, TCP/UDP, SSL, DNS, FTP/TELNET, NIS/NFS, SNMP, email, web) +- Learn fundamentals of firewalls, security tools, and secure payment systems +- Gain hands-on experience through case studies and programming projects + +## Current Instructor + +{{< people tag="cmpe526" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe526" cols="3">}} diff --git a/content/graduate/courses/cmpe528/_index.md b/content/graduate/courses/cmpe528/_index.md new file mode 100644 index 00000000..c43ce1b7 --- /dev/null +++ b/content/graduate/courses/cmpe528/_index.md @@ -0,0 +1,41 @@ +--- +title: CMPE528 +description: Graph Algorithms +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe528/1400 +aliases: + - graduate/courses/cmpe528 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE528 | +| Course Title | Graph Algorithms | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +NP-Completeness proofs for graph-theoretic problems. Connectivity. Network flow algorithms. Algebraic graph theory and its applications: Laplacian matrix, graph partitioning, seriation problem, web page ranking. Vertex ordering for sparse matrix factorization. + +## Course Learning Outcomes + +- Understand NP-completeness in the context of graph-theoretic problems +- Learn connectivity and network flow algorithms +- Gain knowledge of algebraic graph theory concepts +- Understand Laplacian matrices and applications such as graph partitioning and seriation +- Learn web page ranking methods from a graph perspective +- Understand vertex ordering techniques for sparse matrix diff --git a/content/graduate/courses/cmpe529/_index.md b/content/graduate/courses/cmpe529/_index.md new file mode 100644 index 00000000..655d9def --- /dev/null +++ b/content/graduate/courses/cmpe529/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE529 +description: Concurrency Control and Recovery in Databases +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe529/1400 +aliases: + - graduate/courses/cmpe529 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE529 | +| Course Title | Concurrency Control and Recovery in Databases | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter":"Letter Grade" | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Theory of serializability. Various concurrency control algorithms and their proofs of correctness. Recovery in centralized and distributed databases. + +## Course Learning Outcomes + +- Understand the theory of serializability +- Learn major concurrency control algorithms +- Analyze proofs of correctness for concurrency mechanisms +- Understand recovery concepts in centralized databases +- Learn recovery techniques for distributed databases + +## Current Instructor + +{{< people tag="cmpe529" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe529" cols="3">}} diff --git a/content/graduate/courses/cmpe530/_index.md b/content/graduate/courses/cmpe530/_index.md new file mode 100644 index 00000000..8533c029 --- /dev/null +++ b/content/graduate/courses/cmpe530/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE530 +description: Mathematical Foundations of Artificial Intelligence +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe530/1400 +aliases: + - graduate/courses/cmpe530 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE530 | +| Course Title | Mathematical Foundations of Artificial Intelligence | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Signal terminology. Empirical modeling and approximation. Probability concept and signal characteristics for classification. Random processes and decision making. Fundamentals of learning. Optimization theory in classification. Information theory and fuzzy concept applications. + +## Course Learning Outcomes + +- Understand signal terminology and empirical modeling +- Learn approximation techniques relevant to AI +- Understand probability concepts for classification +- Analyze random processes for decision making +- Learn fundamentals of machine learning +- Understand optimization theory in classification tasks +- Gain familiarity with information theory and fuzzy concepts in AI + +## Current Instructor + +{{< people tag="cmpe530" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe530" cols="3">}} diff --git a/content/graduate/courses/cmpe532/_index.md b/content/graduate/courses/cmpe532/_index.md new file mode 100644 index 00000000..48c1c87c --- /dev/null +++ b/content/graduate/courses/cmpe532/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE532 +description: Speech Processing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe532/1400 +aliases: + - graduate/courses/cmpe532 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE532 | +| Course Title | Speech Processing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Man-machine communication. Speech models and representations. Speech synthesis. Speech coding. Speech recognition. Dynamic Time Warping and Hidden Markov Models. Neural networks for speech processing. Speech enhancement. + +## Course Learning Outcomes + +- Understand the fundamentals of speech processing and man-machine communication +- Learn speech models and representations +- Understand techniques for speech synthesis and coding +- Learn fundamentals of speech recognition +- Understand Dynamic Time Warping and Hidden Markov Models +- Learn neural network approaches for speech processing +- Gain familiarity with speech enhancement techniques + +## Current Instructor + +{{< people tag="cmpe532" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe532" cols="3">}} diff --git a/content/graduate/courses/cmpe537/_index.md b/content/graduate/courses/cmpe537/_index.md new file mode 100644 index 00000000..a20f31ac --- /dev/null +++ b/content/graduate/courses/cmpe537/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE537 +description: Computer Vision +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe537/1400 +aliases: + - graduate/courses/cmpe537 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE537 | +| Course Title | Computer Vision | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +. + +## Course Learning Outcomes + +- Understand fundamental concepts in computer vision +- Learn basic approaches for image processing and feature extraction +- Gain familiarity with object detection and recognition techniques +- Understand geometric vision concepts such as camera models and transformations +- Learn modern computer-vision techniques including deep learning methods + +## Current Instructor + +{{< people tag="cmpe537" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe537" cols="3">}} diff --git a/content/graduate/courses/cmpe538/_index.md b/content/graduate/courses/cmpe538/_index.md new file mode 100644 index 00000000..ccd495c2 --- /dev/null +++ b/content/graduate/courses/cmpe538/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE538 +description: 3D Computer Vision +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe538/1400 +aliases: + - graduate/courses/cmpe538 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE538 | +| Course Title | 3D Computer Vision | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +3D scanning sensors, software and systems. Multi-view geometry and camera calibration. 3D registration. Shape reconstruction from point clouds. 3D representation and modeling, 3D shape analysis, 3D matching and recognition. Applications of 3D computer vision. + +## Course Learning Outcomes + +- Understand 3D scanning sensors, systems, and software +- Learn multi-view geometry and camera calibration techniques +- Understand 3D registration and alignment methods +- Learn shape reconstruction from point clouds +- Gain familiarity with 3D representation and modeling +- Understand 3D shape analysis and recognition methods +- Explore applications of 3D computer vision in real-world systems + +## Current Instructor + +{{< people tag="cmpe538" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe538" cols="3">}} diff --git a/content/graduate/courses/cmpe540/_index.md b/content/graduate/courses/cmpe540/_index.md new file mode 100644 index 00000000..3c29f205 --- /dev/null +++ b/content/graduate/courses/cmpe540/_index.md @@ -0,0 +1,49 @@ +--- +title: CMPE540 +description: Principles of Artificial Intelligence +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe540/1400 +aliases: + - graduate/courses/cmpe540 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE540 | +| Course Title | Principles of Artificial Intelligence | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +General problem solving methods in artificial intelligence. Search methods. Production systems. Games and heuristics. Knowledge representation. Artificial Intelligence languages. + +## Course Learning Outcomes + +- Learn general AI problem-solving methods +- Understand classical search strategies +- Gain familiarity with production systems +- Learn game-playing algorithms and heuristics +- Understand core approaches in knowledge representation +- Learn historical and modern Artificial Intelligence languages + +## Current Instructor + +{{< people tag="cmpe540" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe540" cols="3">}} diff --git a/content/graduate/courses/cmpe542/_index.md b/content/graduate/courses/cmpe542/_index.md new file mode 100644 index 00000000..28a7f7ed --- /dev/null +++ b/content/graduate/courses/cmpe542/_index.md @@ -0,0 +1,51 @@ +--- +title: CMPE542 +description: Automated Theorem Proving +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe542/1400 +aliases: + - graduate/courses/cmpe542 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE542 | +| Course Title | Automated Theorem Proving | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Review of propositional and first-order logic. Herbrand's theorem. The resolution principle. Semantic resolution and lock resolution. Linear resolution. The equality relation. Some proof procedures based on Herbrand's theorem. Program analysis. Deductive question answering, problem solving and program synthesis. Implementation of a theorem prover. + +## Course Learning Outcomes + +- Review propositional and first-order logic foundations +- Understand Herbrand’s theorem and its role in theorem proving +- Learn the resolution principle and its variants (semantic, lock, linear) +- Understand the treatment of equality in automated reasoning +- Gain familiarity with proof procedures based on Herbrand’s theorem +- Learn concepts of program analysis through theorem proving +- Understand deductive question answering and problem solving +- Gain experience in implementing components of a theorem prover + +## Current Instructor + +{{< people tag="cmpe542" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe542" cols="3">}} diff --git a/content/graduate/courses/cmpe544/_index.md b/content/graduate/courses/cmpe544/_index.md new file mode 100644 index 00000000..3ab3d167 --- /dev/null +++ b/content/graduate/courses/cmpe544/_index.md @@ -0,0 +1,45 @@ +--- +title: CMPE544 +description: Pattern Recognition +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe544/1400 +aliases: + - graduate/courses/cmpe544 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE544 | +| Course Title | Pattern Recognition | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Bayes decision theory. Parametric and nonparametric methods. Linear discriminant functions. Higher order discriminants with emphasis on artificial neural network-based learning methods. Unsupervised learning and clustering. Case study: Vision. + +## Course Learning Outcomes + +- Understand Bayes decision theory for classification +- Learn parametric and nonparametric pattern recognition methods +- Understand linear discriminant functions +- Learn higher-order discriminants and ANN-based approaches +- Understand unsupervised learning and clustering +- Explore case studies, especially in computer vision + +## Current Instructor + +{{< people tag="cmpe544" cols= diff --git a/content/graduate/courses/cmpe545/_index.md b/content/graduate/courses/cmpe545/_index.md new file mode 100644 index 00000000..99fe5626 --- /dev/null +++ b/content/graduate/courses/cmpe545/_index.md @@ -0,0 +1,39 @@ +--- +title: CMPE545 +description: Artificial Neural Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe545/1400 +aliases: + - graduate/courses/cmpe545 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE545 | +| Course Title | Artificial Neural Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Introduction to cognitive science. Parallel, distributed problems. Constraint satisfaction. Hopfield model. Supervised vs. unsupervised learning. Single vs. multi-layer perceptrons. Static vs. dynamic network architecture. Comparison of neural approaches with parametric and nonparametric statistical methods. Neural network applications. + +## Course Learning Outcomes + +- Understand foundations of cognitive science relevant to neural networks +- Learn parallel and distributed problem-solving approaches +- Understand constraint satisfaction and the Hopfield model +- Learn differences be diff --git a/content/graduate/courses/cmpe547/_index.md b/content/graduate/courses/cmpe547/_index.md new file mode 100644 index 00000000..6e9438ab --- /dev/null +++ b/content/graduate/courses/cmpe547/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE547 +description: Bayesian Statistics and Machine Learning +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe547/1400 +aliases: + - graduate/courses/cmpe547 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE547 | +| Course Title | Bayesian Statistics and Machine Learning | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Machine learning approaches using Bayesian statistics. Graphical models — directed and undirected models, learning and inference. Hidden Markov Models, Linear Dynamical Systems, Kalman filtering and smoothing, message passing algorithms. Junction Tree, factor graphs, sum-product, hierarchical Bayesian modeling, Expectation-Maximisation, and Variational Approximation techniques. + +## Course Learning Outcomes + +- Understand Bayesian approaches to machine learning +- Learn graphical models (directed & undirected) and inference methods +- Understand Hidden Markov Models and Linear Dynamical Systems +- Learn Kalman filtering, smoothing, and message-passing algorithms +- Understand Junction Tree, factor graphs, and sum-product algorithm +- Learn hierarchical Bayesian modeling +- Gain familiarity with EM and variational approximation techniques + +## Current Instructor + +{{< people tag="cmpe547" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe547" cols="3">}} diff --git a/content/graduate/courses/cmpe548/_index.md b/content/graduate/courses/cmpe548/_index.md new file mode 100644 index 00000000..bd07cfde --- /dev/null +++ b/content/graduate/courses/cmpe548/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE548 +description: Monte Carlo Methods +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe548/1400 +aliases: + - graduate/courses/cmpe548 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE548 | +| Course Title | Monte Carlo Methods | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Basic principles of generating random variates, rejection, reweighting and variance reduction, importance sampling and rejection control. Monte Carlo computational strategies: Sequential Monte Carlo (SMC), Markov Chain Monte Carlo (MCMC), Metropolis–Hastings algorithm, reversible jump process, Gibbs sampler, simulated annealing and bridging. Population Monte Carlo, Markov chains and convergence, Annealed importance sampling and SMC samplers. + +## Course Learning Outcomes + +- Understand principles of generating random variates +- Learn rejection sampling, reweighting, and variance reduction +- Understand importance sampling and rejection-control methods +- Learn Monte Carlo computational strategies including SMC and MCMC +- Understand Metropolis–Hastings, Gibbs sampling, and reversible jump methods +- Learn simulated annealing, bridging, and population Monte Carlo +- Understand Markov chains, convergence, AIS, and SMC samplers + +## Current Instructor + +{{< people tag="cmpe548" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe548" cols="3">}} diff --git a/content/graduate/courses/cmpe549/_index.md b/content/graduate/courses/cmpe549/_index.md new file mode 100644 index 00000000..ba966501 --- /dev/null +++ b/content/graduate/courses/cmpe549/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE549 +description: Bioinformatics +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe549/1400 +aliases: + - graduate/courses/cmpe549 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE549 | +| Course Title | Bioinformatics | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Fundamentals of bioinformatics; pairwise and multiple sequence alignment; similarity and search algorithms for biological sequences; motif finding; phylogenetic analysis; genome assembly algorithms; data and text mining for biomedical sciences. + +## Course Learning Outcomes + +- Understand fundamentals of bioinformatics +- Learn pairwise and multiple sequence alignment techniques +- Understand similarity and search algorithms for biological sequences +- Learn motif-finding methods +- Understand phylogenetic analysis +- Gain familiarity with genome assembly algorithms +- Learn basics of data and text mining for biomedical sciences + +## Current Instructor + +{{< people tag="cmpe549" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe549" cols="3">}} diff --git a/content/graduate/courses/cmpe556/_index.md b/content/graduate/courses/cmpe556/_index.md new file mode 100644 index 00000000..0f5acf4e --- /dev/null +++ b/content/graduate/courses/cmpe556/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE556 +description: Complex Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe556/1400 +aliases: + - graduate/courses/cmpe556 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE556 | +| Course Title | Complex Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Familiarity with graph theory | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Random, Regular, Scale-Free, Small-World networks. Empirical studies, metrics, models and applications of Complex Networks. Clusters, community detection, and examples of complex networks: social networks, biological networks, Internet, WWW. + +## Course Learning Outcomes + +- Understand major types of complex networks (random, regular, scale-free, small-world) +- Learn empirical methods, metrics, and models of complex networks +- Understand clustering and community detection approaches +- Explore real-world complex networks: social, biological, Internet, WWW +- Analyze topological properties and dynamics of complex networks + +## Current Instructor + +{{< people tag="cmpe556" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe556" cols="3">}} diff --git a/content/graduate/courses/cmpe557/_index.md b/content/graduate/courses/cmpe557/_index.md new file mode 100644 index 00000000..89d57453 --- /dev/null +++ b/content/graduate/courses/cmpe557/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE557 +description: Complex Systems +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe557/1400 +aliases: + - graduate/courses/cmpe557 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE557 | +| Course Title | Complex Systems | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Complexity, self-organization, emergence, chaotic systems, complex adaptive systems, nonlinear systems, and models for complex systems. + +## Course Learning Outcomes + +- Understand the concept of complexity and its manifestations +- Learn principles of self-organization and emergence +- Understand chaotic systems and nonlinear dynamics +- Learn fundamentals of complex adaptive systems +- Explore modeling approaches for complex systems + +## Current Instructor + +{{< people tag="cmpe557" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe557" cols="3">}} diff --git a/content/graduate/courses/cmpe561/_index.md b/content/graduate/courses/cmpe561/_index.md new file mode 100644 index 00000000..6eb4234f --- /dev/null +++ b/content/graduate/courses/cmpe561/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE561 +description: Natural Language Processing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe561/1400 +aliases: + - graduate/courses/cmpe561 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE561 | +| Course Title | Natural Language Processing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +There has been a striking growth in text data such as web pages, news articles, e-mail messages, social media data, and scientific publications in recent years. Developing tools for processing and utilizing this huge amount of textual information is increasingly important. This course covers techniques for processing and making sense of text data written in natural (human) language. Core tasks in natural language processing include language modeling, syntactic analysis, semantic interpretation, and discourse analysis. Applications include information extraction, question answering, summarization, and sentiment analysis. + +## Course Learning Outcomes + +- Understand the fundamental challenges and applications of NLP +- Learn language modeling, syntactic parsing, and semantic interpretation +- Understand discourse analysis techniques +- Explore NLP applications such as information extraction, QA, summarization, and sentiment analysis +- Learn how to process and analyze large-scale text data + +## Current Instructor + +{{< people tag="cmpe561" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe561" cols="3">}} diff --git a/content/graduate/courses/cmpe565/_index.md b/content/graduate/courses/cmpe565/_index.md new file mode 100644 index 00000000..270d6605 --- /dev/null +++ b/content/graduate/courses/cmpe565/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE565 +description: Autonomous Robots +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe565/1400 +aliases: + - graduate/courses/cmpe565 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE565 | +| Course Title | Autonomous Robots | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Robotic hardware: sensors and actuators. Review of control methods. Intelligent control approaches. Learning and planning. Robotic architectures: classical, reactive, behavior-based, and hybrid. Multi-agent systems. + +## Course Learning Outcomes + +- Understand sensors, actuators, and robotic hardware fundamentals +- Review classical and intelligent control approaches +- Learn planning and learning methods in robotics +- Understand classical, reactive, behavior-based, and hybrid robotic architectures +- Explore multi-agent robotic systems and coordination + +## Current Instructor + +{{< people tag="cmpe565" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe565" cols="3">}} diff --git a/content/graduate/courses/cmpe567/_index.md b/content/graduate/courses/cmpe567/_index.md new file mode 100644 index 00000000..a9a42cd0 --- /dev/null +++ b/content/graduate/courses/cmpe567/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE567 +description: Broadband Wireless Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe567/1400 +aliases: + - graduate/courses/cmpe567 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE567 | +| Course Title | Broadband Wireless Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Concepts and research topics in emerging wireless broadband networks. Wireless local area networks, wireless metropolitan area networks, wireless regional area networks. Cognitive radio with emphasis on sensing, mobility, and seamless operation. + +## Course Learning Outcomes + +- Understand concepts in emerging broadband wireless networks +- Learn fundamentals of WLANs, WMANs, and WRANs +- Understand cognitive radio principles including sensing and mobility +- Analyze seamless operation challenges in wireless broadband systems + +## Current Instructor + +{{< people tag="cmpe567" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe567" cols="3">}} diff --git a/content/graduate/courses/cmpe579/_index.md b/content/graduate/courses/cmpe579/_index.md new file mode 100644 index 00000000..3235a12a --- /dev/null +++ b/content/graduate/courses/cmpe579/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE579 +description: Graduate Seminar +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe579/1400 +aliases: + - graduate/courses/cmpe579 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE579 | +| Course Title | Graduate Seminar | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 0, PS:1, Labs:0 | +| Course Credits | 0 | +| ECTS | 1 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +The widening of students' perceptiveness and awareness of topics of interest to computer engineers through seminars offered by faculty, guest speakers, and graduate students. + +## Course Learning Outcomes + +- Gain exposure to current research topics in Computer Engineering +- Develop awareness of emerging technologies and research directions +- Learn from faculty, guest speakers, and peers through seminar presentations +- Improve academic presentation and discussion skills + +## Current Instructor + +{{< people tag="cmpe579" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe579" cols="3">}} diff --git a/content/graduate/courses/cmpe580/_index.md b/content/graduate/courses/cmpe580/_index.md new file mode 100644 index 00000000..345b01f6 --- /dev/null +++ b/content/graduate/courses/cmpe580/_index.md @@ -0,0 +1,61 @@ +--- +title: CMPE580 +description: Special Topics: Personal Electronic Health Assistants +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe580/1400 +aliases: + - graduate/courses/cmpe580 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE580 | +| Course Title | Special Topics: Personal Electronic Health Assistants | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Many of our behavior patterns are key determinants of important health outcomes. Positive health effects can be achieved when indicators of an individual’s lifestyle and behavior are maintained within healthy ranges. + +Personal electronic health assistants support health and wellness in everyday life. +This lecture provides an introduction to such systems by combining relevant methods, tools, and practical applications from both research and industry. + +Covered methods include physiological signal processing, data privacy techniques, and relevant approaches from machine learning and data mining. + +Applications include personal stress assistants, ambient assisted living, and smartphone-based health assistance systems. + +No special requirements are needed to attend this course; all necessary background knowledge is introduced within the lectures. + +For more information: +**http://www.barnrich.ch/wiki/doku.php?id=pub:lectures:start** + +## Course Learning Outcomes + +- Understand the role of behavior patterns in personal health and wellness +- Learn the principles behind personal electronic health assistant systems +- Gain familiarity with physiological signal processing +- Understand data privacy considerations in health applications +- Learn machine learning and data mining methods for health technologies +- Explore applications such as stress assistants, ambient assisted living, and mobile health systems + +## Current Instructor + +{{< people tag="cmpe580" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe580" cols="3">}} diff --git a/content/graduate/courses/cmpe581/_index.md b/content/graduate/courses/cmpe581/_index.md new file mode 100644 index 00000000..78005fa2 --- /dev/null +++ b/content/graduate/courses/cmpe581/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE581 +description: Special Topics: Computer Engineering for Mobile/Wireless Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe581/1400 +aliases: + - graduate/courses/cmpe581 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE581 | +| Course Title | Special Topics: Computer Engineering for Mobile/Wireless Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The specific topic is chosen from among the recent technological or theoretical developments in mobile and wireless networks. + +## Course Learning Outcomes + +- Understand advanced concepts in mobile and wireless networking +- Gain exposure to current research topics in the field +- Learn how emerging technologies influence computer engineering solutions +- Explore specialized problems and applications within wireless systems + +## Current Instructor + +{{< people tag="cmpe581" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe581" cols="3">}} diff --git a/content/graduate/courses/cmpe582/_index.md b/content/graduate/courses/cmpe582/_index.md new file mode 100644 index 00000000..e6ef2da0 --- /dev/null +++ b/content/graduate/courses/cmpe582/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE582 +description: Special Topics: Satellite Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe582/1400 +aliases: + - graduate/courses/cmpe582 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE582 | +| Course Title | Special Topics: Satellite Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The topic will be selected from recent technological or theoretical developments, with emphasis on satellite networks. + +## Course Learning Outcomes + +- Understand emerging concepts in satellite networking +- Explore current technological and theoretical developments in satellite communication +- Learn applications and challenges of satellite-based network systems +- Gain exposure to specialized research topics in the field + +## Current Instructor + +{{< people tag="cmpe582" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe582" cols="3">}} diff --git a/content/graduate/courses/cmpe584/_index.md b/content/graduate/courses/cmpe584/_index.md new file mode 100644 index 00000000..c5929ed1 --- /dev/null +++ b/content/graduate/courses/cmpe584/_index.md @@ -0,0 +1,50 @@ +--- +title: CMPE584 +description: Special Topics: Reconfigurable Computing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe584/1400 +aliases: + - graduate/courses/cmpe584 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE584 | +| Course Title | Special Topics: Reconfigurable Computing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Field Programmable Gate Arrays (FPGAs). Configuration vs reconfiguration. Spatial vs temporal computation. Dataflow computing. Embedded system design on FPGAs. Processor customization. Methods and tools. + +## Course Learning Outcomes + +- Understand FPGA architectures and fundamentals +- Learn configuration vs. reconfiguration paradigms +- Understand spatial vs. temporal computation models +- Learn dataflow computing concepts +- Gain experience in embedded system design on FPGAs +- Understand processor customization techniques +- Explore tools and methodologies for reconfigurable computing + +## Current Instructor + +{{< people tag="cmpe584" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe584" cols="3">}} diff --git a/content/graduate/courses/cmpe585/_index.md b/content/graduate/courses/cmpe585/_index.md new file mode 100644 index 00000000..5c495b5c --- /dev/null +++ b/content/graduate/courses/cmpe585/_index.md @@ -0,0 +1,58 @@ +--- +title: CMPE585 +description: Special Topics: Wearable Computing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe585/1400 +aliases: + - graduate/courses/cmpe585 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE585 | +| Course Title | Special Topics: Wearable Computing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Wearable computers align with Mark Weiser’s vision of technologies that become woven into everyday life. +This course introduces wearable computing by integrating methods, tools, and practical applications from research and industry. + +Methods include wearable and ambient sensor data processing, basic statistical techniques, and machine learning and data mining approaches. + +Applications cover activity recognition in daily life, human emotion detection, social signal processing, and the smartphone as a wearable computing device. + +There are no special requirements to attend this lecture. +More information: +**http://www.barnrich.ch/wiki/doku.php?id=pub:lectures:start** + +## Course Learning Outcomes + +- Understand fundamental concepts of wearable computing +- Learn sensor data processing for wearable and ambient systems +- Apply statistical, ML, and data-mining methods to wearable data +- Explore applications such as activity recognition and emotion detection +- Understand social signal processing and mobile/wearable systems +- Gain familiarity with real-world wearable computing use cases + +## Current Instructor + +{{< people tag="cmpe585" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe585" cols="3">}} diff --git a/content/graduate/courses/cmpe586/_index.md b/content/graduate/courses/cmpe586/_index.md new file mode 100644 index 00000000..3375a66b --- /dev/null +++ b/content/graduate/courses/cmpe586/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE586 +description: Special Topics: Wireless Information Networks +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe586/1400 +aliases: + - graduate/courses/cmpe586 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE586 | +| Course Title | Special Topics: Wireless Information Networks | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The topic will be selected from recent technological or theoretical developments related to wireless information networks. + +## Course Learning Outcomes + +- Understand advanced and emerging topics in wireless information networks +- Learn modern architectures, protocols, and system designs +- Explore current research trends in wireless networking +- Analyze engineering problems unique to wireless information systems + +## Current Instructor + +{{< people tag="cmpe586" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe586" cols="3">}} diff --git a/content/graduate/courses/cmpe587/_index.md b/content/graduate/courses/cmpe587/_index.md new file mode 100644 index 00000000..5f26c60f --- /dev/null +++ b/content/graduate/courses/cmpe587/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE587 +description: Special Topics: Introduction to Research in Theoretical Computing +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe587/1400 +aliases: + - graduate/courses/cmpe587 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE587 | +| Course Title | Special Topics: Introduction to Research in Theoretical Computing | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The topic will be chosen from recent technological or theoretical developments related to theoretical computing. + +## Course Learning Outcomes + +- Gain exposure to current research topics in theoretical computer science +- Understand foundational concepts and emerging developments +- Explore modern theoretical models, tools, and methods +- Learn how theoretical research connects to real-world computing problems + +## Current Instructor + +{{< people tag="cmpe587" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe587" cols="3">}} diff --git a/content/graduate/courses/cmpe588/_index.md b/content/graduate/courses/cmpe588/_index.md new file mode 100644 index 00000000..2a509099 --- /dev/null +++ b/content/graduate/courses/cmpe588/_index.md @@ -0,0 +1,47 @@ +--- +title: CMPE588 +description: Special Topics: Testing and Verification Techniques for Machine Learning +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe588/1400 +aliases: + - graduate/courses/cmpe588 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE588 | +| Course Title | Special Topics: Testing and Verification Techniques for Machine Learning | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | None | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The topic will be chosen from among recent technological or theoretical developments related to testing and verification techniques for machine learning systems. + +## Course Learning Outcomes + +- Understand testing methodologies for machine learning systems +- Learn verification techniques applied to ML models +- Explore recent developments in trustworthy and safe ML +- Gain familiarity with challenges in evaluating complex learning systems + +## Current Instructor + +{{< people tag="cmpe588" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe588" cols="3">}} diff --git a/content/graduate/courses/cmpe589/_index.md b/content/graduate/courses/cmpe589/_index.md new file mode 100644 index 00000000..271f67e4 --- /dev/null +++ b/content/graduate/courses/cmpe589/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE589 +description: Special Topics: Software Testing and Verification +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe589/1400 +aliases: + - graduate/courses/cmpe589 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE589 | +| Course Title | Special Topics: Software Testing and Verification | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS:0, Labs:0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | Consent of the Instructor | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Study of special topics in computer engineering. The topic will be chosen from among recent technological or theoretical developments related to software testing and verification. + +## Course Learning Outcomes + +- Understand modern software testing methodologies +- Learn formal and informal verification techniques +- Explore research topics in software quality assurance +- Analyze reliability, correctness, and safety concerns in software systems +- Gain familiarity with emerging tools and approaches for software verification + +## Current Instructor + +{{< people tag="cmpe589" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe589" cols="3">}} diff --git a/graduate/courses/cmpe521/_index.md b/graduate/courses/cmpe521/_index.md new file mode 100644 index 00000000..200d0399 --- /dev/null +++ b/graduate/courses/cmpe521/_index.md @@ -0,0 +1,48 @@ +--- +title: CMPE521 +description: Principles of Database Systems +metadata: none +# thumbnail: https://picsum.photos/seed/cmpe521/1400 +aliases: + - graduate/courses/cmpe521 +--- + +## Course Information + + +{{< table class="table-hover table-sm" >}} +||| +| :-- | :-- | +| Faculty | Faculty of Engineering | +| Course Code | CMPE521 | +| Course Title | Principles of Database Systems | +| Language of Instruction | English | +| Course Semester | — | +| Course Hours | Lecture: 3, PS: 0, Labs: 0 | +| Course Credits | 3 | +| ECTS | 10 | +| Grading Mode | Letter Grade | +| Prerequisites | CMPE 321 | +| Corequisites | None | +{{< /table >}} + + +## Catalog Description + +Fundamental concepts of data modeling and popular data models. Design theory for relational databases. Query optimization and data manipulation languages. Concurrency and protection. + +## Course Learning Outcomes + +- Understand fundamental concepts of data modeling and popular data models +- Learn design theory for relational databases +- Gain familiarity with query optimization techniques +- Understand data manipulation languages in database systems +- Learn basic concepts of concurrency control and protection + +## Current Instructor + +{{< people tag="cmpe521" cols="2">}} + +## Previous Instructors + +{{< people_alt tag="former-cmpe521" cols="3">}}