Course Learning Outcomes:

1. Construct a syntactic proof as a series of rigorous inference steps in a logical deduction.
2. Represent logical statements and descriptions declaratively in a logical language.
3. Analyze the semantics of a set of logical statements in a logical framework.
4. Synthesize a rigorous human-understandable English description of a set of logical statements.

Course Learning Outcomes:

1. Design and construct object-oriented programs of 500 lines or less.
2. Apply the programming practices of good style, documentation, testing, encapsulation, inheritance.
3. Show the consequences of numeric representation on computers.

Course Learning Outcomes:

1. Design specifications for the functional behaviour of algorithmic code fragments.
2. Formally verify the correctness of simple code fragments involving conditional statements and loops using logic based techniques.
3. Design regular expressions, state transition diagrams and context-free grammars for formal languages.
4. Appraise the limitations of regular expressions and context-free grammars and to demonstrate that a language is nonregular.
5. Appraise the limitations of algorithmic computation and prove that the halting problem is unsolvable.

Course Learning Outcomes:

1. Elicit stakeholder and requirements information about the system from a variety of sources using a variety of techniques.
2. Analyze requirements information for conflict, ambiguity, completeness, consistency, and evolvability.
3. 3 Compare and contrast different techniques for inspection, static analysis, and testing.
4. Explain different metrics used to evaluate software complexity.
5. Prepare quality assurance plans to validate the final product against the requirements.

Course Learning Outcomes:

1. Design inductive model building (data analytics) algorithms appropriate for systems in a number of application domains.
2. Evaluate the performance of such algorithms, the quality of the models built, and the implications for real-world systems about which data is collected.
3. Justify and interpret the results of analytic processes to those who can leverage them, for example policy makers, senior managers, or scientists.

Course Learning Outcomes:

1. Design and construct medium-sized console and GUI programs in both procedural and object-oriented paradigms.
2. Design and construct 500 to 1000 line console and GUI C++ programs using both Object Oriented and procedural C++ code.
3. Contribute to the design and construction of a larger software project as a member of an agile programming team.
4. Use software engineering techniques and tools to aid in the development of larger scale software projects.

Course Learning Outcomes:

1. Recognize and describe the various architecture styles and design patterns.
2. Analyze the tradeoffs between competing software architecture styles and design patterns.
3. Recover, document, present, and discuss the architecture of a pre-existing software system using high level diagrams (such as UML and basic box and arrow diagrams).
4. Specify, design and possibly implement a feature in a pre-existing code base.
5. Work effectively in a group setting.

Course Learning Outcomes:

1. Learn some foundations about operating systems: process management, interrupts handling, context-switching, and threads.
2. Learn the concept of synchronization and how to enforce synchronization using different synchronization tools. e.g., semaphores.
3. Learn how operating systems manage resource allocation in a time-sharing system, and how to deal with deadlocks.
4. Learn different memory management schemes and how operating systems manage the memory.
5. Learn how to design and develop safe multithreaded applications.
6. Learn basic concepts in operating system security.

Course Learning Outcomes:

1. Critique the usability of an interactive system.
2. Design for people with diverse abilities.
3. Apply a user-centered design process.
4. Design and prototype Graphical User Interfaces.
5. Perform user evaluation of user interfaces.

Course Learning Outcomes:

1. Choose appropriate software process models.
2. Describe the inspection process, and the advantages of inspection.
3. Compare and contrast different testing methods, including black and white box approaches.
4. Explain different metrics used to evaluate software complexity.
5. Outline and implement quality requirements at different phases of the software life-cycle.
6. Build a foundation for further learning by exposure to multiple computer languages, development tools, and methodologies.

Course Learning Outcomes:

1. Explain and compare different formal specification languages based on formalisms such as propositional, predicate, and temporal logic; relational calculus; state machines; and automata.
2. Explain and compare different formal analysis techniques.
3. Discuss techniques to define and implement a specification language.
4. Discuss the use of formal specifications throughout the software lifecycle.

Course Learning Outcomes:

1. Explain the principles of image-guided interventions and AI techniques used in solving problems including segmentation, registration, navigation, and decision support.
2. Analyze clinical problems to identify appropriate computational methods for guiding diagnostic or therapeutic interventions using medical imaging and spatial tracking data.
3. Design and prototype components of image-guided intervention systems that incorporate essential computing methods and AI models.
4. Implement and evaluate key algorithms and modules used in AI-enabled health interventions, demonstrating practical skills in system design, implementation and analysis.

Course Learning Outcomes:

1. Apply database design techniques and tools to create a database schema and database instance for an application.
2. Apply an application design method and development tools to create a web application to support users in querying and manipulating the database for an application.
3. Compose queries on a database in both SQL and the relational algebra.
4. Assess the quality of database schemas and queries.
5. Apply methods to improve the performance and security of a database application.

Course Learning Outcomes:

1. To provide an in-depth knowledge on the design of advanced data analytics techniques.
2. To discuss how to design data pipelines for real-world applications and what are the key factors to consider.
3. To understand implementation of data solutions involving advanced analytics techniques and diagnose problems.
4. To critically analysis of existing data solutions and design new data analysis pipelines in context.

Course Learning Outcomes:

1. Possess a strong understanding of computational complexity, up to and including: Polynomial time Reducibility; classes P and NP; NP-Completeness; Proofs of NP-Completeness.
2. Be able to recognize classical NP-Complete problems.
3. Be able to prove that new problems are NP-Complete using polynomial time reductions from known NP-Complete problems.
4. Apply key algorithm paradigms, both in the abstract and through concrete examples: Divide and Conquer; Greedy Algorithms; Dynamic Programming; Branch and Bound.
5. Have some understanding of an advanced topic, which varies from year to yearRecent choices have included: Linear Programming; Computational Geometry; String Matching.

Course Learning Outcomes:

1. Use and explain formal specification languages based on, e.g., propositional logic, predicate logic, relational calculus, and finite state machines.
2. Use and explain notations and techniques to define the semantics of a language precisely.
3. Use and explain analysis techniques for formal specification languages such as theorem proving, satisfiability checking, automatic test input generation, and exhaustive state space exploration together with their capabilities and limitations.
4. Use and explain tools supporting formal specification languages together with their capabilities and limitations.
5. Design, construct, and analyze small formal specifications.
6. Explain the advantages and disadvantages of formal specification languages and tools.
7. Explain the role and potential uses of formal methods for different software development activities.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Create distributed storage structures for complex datasets.
2. Organize, integrate and process data from distributed storage systems.
3. Create metadata for complex datasets.
4. Articulate issues in data provenance and curation.
5. Build workflows and query the results.

Course Learning Outcomes:

1. CLOs coming soon; please refer to your course syllabus in the meantime.

Course Learning Outcomes:

1. Explain the concepts behind the operation of biological neurons and the evolution of artifical neural network (ANN) to model connections as a representation of information.
2. Design and implement different types of ANNs using a variety of design techniques and learning algorithms for prediction, clustering, classification, storage, and function approximation
3. Apply optimization techniques such as simulated annealing and genetic algorithms with ANN training algorithms
4. Describe with reference to recent research work how ANNs are used to simulate human cognition, vison, and memory, and are applied to intelligent systmes for language and image processing, decision support systems, and predictive systems.
5. Explain the power and limitations of neural network systems.

Course Learning Outcomes:

1. Demonstrate the ability to design and implement interactive computer graphics programs.
2. Understand vector operations, matrix operations, coordinate systems, and transformations between coordinate systems.
3. Apply homogeneous linear transformations in the graphics pipeline to convert 3D points in a virtual world to pixel positions.
4. Evaluate different lighting models and texturing methods to achieve different visual effects.
5. Understand and implement various animation algorithms.
6. Evaluate different random sampling methods in ray tracing for efficiency and accuracy. Implement acceleration methods for ray tracing.

Course Learning Outcomes:

1. Select and implement algorithms for geometric image transformation.
2. Select and implement spatial-domain images filtering methods for different problems.
3. Apply frequency-domain techniques to extract information from images and to edit images.
4. Develop an image segmentation solution using various algorithms.
5. Evaluate and combine image compression techniques.

Course Learning Outcomes:

1. Use real software engineering tools to maintain and enhance existing software systems.
2. Apply component-based software engineering and table-driven programming to multi-phase software systems.
3. Follow standard practices for software quality assurance and testing in a small-team project.
4. Employ domain-specific languages and model-driven engineering to develop software.

Course Learning Outcomes:

1. Describe fundamental concepts in health AI, including key data types, biological systems, and computational methods.
2. Compare and contrast different algorithmic approaches used in the analysis of biological and clinical data.
3. Apply core machine learning and statistical techniques to publicly available biological datasets.
4. Apply core machine learning and statistical techniques to publicly available biological datasets.
5. Design and implement computational workflows for solving health-related problems using state-of-the-art algorithms.
6. Design and implement computational workflows for solving health-related problems using state-of-the-art algorithms.

Course Learning Outcomes:

1. Explain the principles and workflows behind medical image analysis techniques, including segmentation, registration, and video tracking.
2. Analyze the limitations and capabilities of AI-based methods in processing and interpreting 2D and 3D medical images.
3. Design computational solutions using AI tools and image analysis techniques to address real-world medical imaging problems.
4. Implement and evaluate image processing pipelines for visualization, segmentation, registration, and motion tracking using imaging data.
5. Interpret performance metrics and results of AI models in the context of image analysis, with attention to validation and generalizability.