Intelligent Decision Systems Engineering

This course provides a graduate-level foundation in the engineering of intelligent decision systems, emphasizing the artificial intelligence theories, computational models, algorithmic methods, and responsible design principles used to build systems that reason, learn, plan, adapt, and support decision-making under conditions of uncertainty and complexity. It examines intelligent agents, search, constraint satisfaction, logical reasoning, knowledge representation, automated planning, probabilistic reasoning, decision theory, machine learning, deep learning, reinforcement learning, natural language processing, robotics, multiagent systems, and the ethical and societal dimensions of AI-enabled decision environments. Designed for advanced study and professional application in computational science, engineering, analytics, automation, and intelligent systems, the course prepares students to analyze, design, evaluate, and communicate intelligent decision architectures that integrate data, models, algorithms, human objectives, and real-world constraints.

This course examines intelligent decision systems as engineered computational systems that perceive environments, represent knowledge, reason under uncertainty, learn from data, plan actions, and support or automate decision-making. It introduces the intellectual foundations of artificial intelligence through the study of intelligent agents, problem solving, search, adversarial reasoning, constraint satisfaction, logic, knowledge representation, automated planning, uncertainty, probabilistic reasoning, and decision-theoretic methods. These foundations enable students to understand how intelligent systems structure problems, evaluate alternatives, and select actions in complex environments where information may be incomplete, objectives may be uncertain, and outcomes may evolve over time.

The course further develops students’ understanding of learning-based and adaptive approaches to decision-system engineering. Major areas include learning from examples, probabilistic models, deep learning, reinforcement learning, natural language processing, robotics, and multiagent decision-making. Students examine how these methods contribute to prediction, classification, optimization, sequential decision-making, autonomous behavior, human–machine interaction, and adaptive control. Emphasis is placed on how different AI methods can be selected, combined, and evaluated within coherent decision architectures rather than treated as isolated technical tools.

A central concern of the course is the responsible engineering of intelligent systems for real-world use. Students analyze how intelligent decision systems operate within technical, organizational, social, and ethical contexts, including issues of reliability, explainability, fairness, privacy, accountability, safety, and human oversight. The course addresses the limitations as well as the capabilities of AI methods, preparing students to make disciplined judgments about system design, performance, deployment, and risk.

By integrating classical AI, probabilistic reasoning, modern machine learning, autonomous systems, and responsible AI principles, the course prepares students for advanced study, research, and professional practice in artificial intelligence, intelligent systems engineering, data science, computational modeling, robotics, automation, and decision analytics. It develops the conceptual depth, technical vocabulary, analytical judgment, and ethical awareness needed to interpret intelligent decision systems as complex infrastructures that connect algorithms, data, human purposes, and engineered action.

This course is designed to:

1. Establish a rigorous graduate-level foundation in artificial intelligence concepts, computational reasoning methods, and intelligent decision-system architectures.

2. Develop students’ understanding of intelligent agents, search, constraint satisfaction, logical reasoning, knowledge representation, automated planning, uncertainty, and probabilistic decision-making.

3. Examine the role of machine learning, deep learning, reinforcement learning, natural language processing, robotics, and multiagent systems in adaptive and autonomous decision environments.

4. Strengthen students’ ability to analyze how AI methods are selected, integrated, and evaluated within engineered systems that operate under complexity, uncertainty, incomplete information, and real-world constraints.

5. Cultivate responsible technical judgment concerning the reliability, transparency, fairness, privacy, safety, accountability, and ethical deployment of intelligent decision systems.

6. Prepare students for advanced study, research, and professional practice in artificial intelligence, intelligent systems engineering, computational analytics, automation, and AI-enabled decision environments.

Russell, S., & Norvig, P. (2022). Artificial intelligence: A modern approach (4th ed.). Pearson.

Access: Pearson

Final grades are determined based on the following weighted components:

• Formative Assessment: 30%

• Summative Assessment: 60%

• Professionalism: 10%

All graded components are expressed as percentages and combined using a weighted average to produce a final numeric grade.

• Exemplary: Demonstrates comprehensive mastery of course learning outcomes.

• Excellent: Demonstrates strong and consistent performance.

• Satisfactory: Meets minimum expectations.

• Minimal Pass: Demonstrates limited but sufficient performance.

• Fail: Does not meet minimum requirements.

Administrative grade marks are assigned in accordance with institutional academic policies.

• The final numeric grade is calculated as a weighted average of all graded components.

• All component scores are recorded as percentages and included in the final calculation.

• Final grades are rounded to one decimal place using standard half-up rounding.

• A minimum final grade of 60.0 is required to pass the course.

• Failure to submit required assessments results in a grade of zero for the missed component, unless an approved accommodation or other authorization granted in accordance with institutional policy applies.

• Approved make-up assessments must be equivalent in scope and rigor to the original assessment.

• Academic misconduct is addressed in accordance with Section 12 (Academic Integrity and Artificial Intelligence Use) and institutional policy. Grade penalties are determined through formal institutional processes and may include a grade of zero for the assessment or a final grade of F for the course.

• Completion of designated major assessment components, where specified in the course assessment structure, is required to pass the course.

• Additional or extra credit activities do not alter the established grading structure unless formally approved and documented in accordance with institutional policy.

This component evaluates observable academic and professional conduct, including:

• attendance and participation

• timely submission of work

• adherence to academic integrity standards

• professional conduct in academic interactions

Evaluation is based on defined criteria within the course assessment structure and associated rubrics. This component does not duplicate or replace formal academic misconduct determinations under Section 12.

• Grade reviews are limited to verification of calculation accuracy and adherence to published assessment criteria.

• Requests for review must follow institutional policy.

• The instructor of record determines grades based on documented evidence, subject to institutional policy.

Academic integrity is the foundation of all scholarly work and requires that all academic activities be conducted with honesty, accountability, and responsibility.

Students must produce original work and appropriately acknowledge the contributions of others. All submitted work must accurately represent the student’s own understanding and effort.

Academic misconduct includes, but is not limited to:

• plagiarism, including the use of another’s words, ideas, or work without proper attribution

• fabrication or falsification of information or data

• unauthorized collaboration or collusion

• cheating in any form of assessment

• misrepresentation of authorship or academic work

All instances of academic misconduct are addressed in accordance with institutional policy and result in disciplinary action as determined through formal institutional processes.

Students are responsible for:

• applying appropriate citation and referencing standards

• acknowledging all sources used in academic work

• ensuring that submitted work reflects their own independent understanding

The use of previously submitted work, whether the student’s own or that of others, is not permitted unless explicitly authorized in course instructions.

Course materials—including lectures, recordings, slides, assessments, and peer contributions—are restricted to enrolled students and may not be shared, distributed, reproduced, or used outside the course without prior written authorization in accordance with institutional policy.

Artificial intelligence systems, automated tools, and external computational assistance may only be used as explicitly authorized in course instructions or assessment guidelines issued in accordance with institutional policy.

Unless explicitly authorized:

• AI-generated or externally generated content may not be submitted as original student work

• such tools may not be used to complete or substantially assist in assessments

• their use must not replace independent critical thinking or academic effort

Where such use is authorized in course instructions in accordance with institutional policy:

• it must be transparently disclosed in accordance with course instructions and institutional policy

• students remain fully responsible for the accuracy, integrity, and originality of all submitted work

Unauthorized or undisclosed use of such tools constitutes academic misconduct.

Students are expected to:

• engage in all academic activities with integrity

• follow all course instructions and assessment requirements

• collaborate only where explicitly authorized

Unauthorized collaboration is considered academic misconduct.

All violations of academic integrity are addressed in accordance with institutional policy.

Sanctions are determined through formal institutional processes based on the severity and nature of the violation and include:

• grade penalties on individual assessments

• a final grade of F for the course

• additional disciplinary actions in accordance with institutional policy

• The University provides reasonable accommodations to ensure equitable access to academic programs for students with documented disabilities or approved learning needs, in accordance with applicable laws and institutional policy.

• Students seeking accommodations must register with Disability Services (DS) and obtain formal approval. Approved accommodations are communicated through official institutional channels.

• Instructors implement only those accommodations that have been formally authorized. Accommodations are not applied without prior approval from Disability Services.

• Accommodations are applied prospectively from the date of approval and are not retroactive unless explicitly authorized by the institution.

• Accommodations are designed to provide equitable access and do not alter essential course requirements, academic standards, or learning outcomes.

• The University may determine that certain accommodations are not appropriate where they would fundamentally alter the nature of the course or impose an undue burden, in accordance with institutional policy. Accessibility accommodations do not exempt students from adherence to academic integrity requirements as defined in Section 12.

Students are responsible for:

• initiating accommodation requests with Disability Services in a timely manner

• providing required documentation directly to Disability Services

• communicating approved accommodations to instructors as required

Delays in initiating requests may affect the timely implementation of accommodations.

Instructors are responsible for:

• implementing approved accommodations as communicated by Disability Services

• maintaining confidentiality of accommodation-related information

• ensuring that accommodations are applied consistently and in accordance with institutional policy

Instructors do not independently evaluate, approve, or modify accommodation requests.

• All course participants are expected to engage in a respectful and inclusive learning environment that supports diverse perspectives, backgrounds, and experiences.

• Discriminatory, exclusionary, or disruptive behavior is not permitted and may be addressed in accordance with institutional policy.

• Students experiencing short-term illness or temporary conditions must notify the instructor as soon as reasonably possible.

• Temporary academic adjustments are provided in accordance with institutional policy and do not replace formal accommodations issued through Disability Services.

• Medical documentation, when required, must be submitted through appropriate institutional channels. Instructors do not collect or retain medical documentation unless explicitly authorized.

• All student information related to accommodations, health, or personal circumstances is handled in accordance with institutional policy.

• Documentation must be submitted through designated institutional services. Confidential information is disclosed only as necessary for academic implementation.

The University provides a comprehensive network of academic and student support services to facilitate academic achievement, skill development, and overall student success.

These services include, but are not limited to:

• library and digital databases providing access to scholarly resources

• writing and research support for academic communication and methodology

• tutoring and academic coaching for subject mastery and study strategies

• technology support for course platforms and digital tools

• counseling and wellness services supporting personal and academic well-being

Students are expected to engage with available support services as part of their academic responsibility.

Utilization of these services supports successful completion of course requirements and aligns with expectations for academic engagement as defined in Section 11.

• The official channels for all course-related communication are:

  • the course Learning Management System (LMS)

  • the student’s institutional email account

• All course announcements, instructions, materials, and updates are communicated through these channels.

• Only communications delivered through official channels are considered valid and binding.

• All course communication is subject to institutional policy, which supersedes any course-level communication.

Students are responsible for:

• monitoring official communication channels consistently and in a timely manner

• reviewing all course announcements and updates promptly upon release

• maintaining active access to institutional communication systems

Failure to review official communications does not exempt students from compliance with course requirements, deadlines, or policies.

• Instructors communicate course-related information through official channels.

• Instructor responses to student inquiries are provided in accordance with institutional policy.

• Instructors are not required to respond to communications sent outside official channels.

Course announcements, assignment instructions, and assessment requirements communicated through official channels constitute formal course directives.

In the event of conflicting information:

• institutional policy governs

• the most recent official communication governs

• course communications issued through the LMS take precedence over all other channels

The LMS is the central platform for:

• accessing course materials

• submitting assignments

• receiving feedback and grades

• participating in course activities

Students are responsible for maintaining access to and functional use of the LMS.

• All assignments must be submitted through the designated platform in accordance with specified instructions.

• System-generated timestamps recorded by the LMS constitute the official record for submission and deadline determination.

• All deadlines are interpreted based on the official system time of the LMS.

• In the event of verified system-wide technical disruptions affecting the LMS or institutional systems:

  • adjustments to deadlines or submission requirements are determined in accordance with institutional policy

• Technical issues must be reported promptly through institutional support services.

• Individual technical issues not verified through institutional systems do not automatically warrant deadline adjustments.

Course adjustments may be made to support the achievement of learning outcomes and maintain academic standards.

All modifications:

• must be communicated through official channels

• must comply with institutional policy

• must not alter the grading structure defined in Section 11

• must not compromise assessment integrity as defined in Section 12

• All course-related communication must be conducted in a professional and respectful manner.

• Unprofessional, disruptive, or inappropriate communication is addressed in accordance with institutional policy and Sections 11 and 12, as applicable.

Engagement in this course is both an academic requirement and a professional obligation, reflecting the intellectual discipline, collegial responsibility, and scholarly integrity expected in graduate study at the University. Students are expected to participate consistently and demonstrate preparation, respect, and analytical rigor in all learning activities.

Participation Expectations

• Students are expected to:

  • attend all scheduled sessions, in accordance with course requirements and institutional policy

  • complete assigned readings and preparatory work in advance

  • contribute meaningfully to academic discussions and learning activities

• Participation is defined by the quality and substance of engagement, including critical thinking, responsiveness to course content, and constructive interaction with peers and faculty.

• Absence from scheduled sessions may affect a student’s ability to meet course requirements and may require additional academic work, where applicable, in accordance with course instructions and institutional policy.

Modes of Attendance

• Online Sessions (e.g., Microsoft Teams):

  • Students must log in punctually and remain actively engaged.

  • Students must use a functioning camera and microphone where required for participation, in accordance with institutional policy and approved accommodations.

  • Cameras must remain on unless otherwise authorized in accordance with institutional policy or approved accommodations.

  • Participation must occur in a distraction-free environment; participation from vehicles or non-academic environments is not permitted except under exceptional circumstances consistent with institutional policy.

• On-Campus Sessions:

  • Students must attend in accordance with the official campus schedule.

  • Students must arrive on time and remain for the full duration of the session.

  • Students are responsible for making appropriate travel and scheduling arrangements.

Classroom Expectations

• Classes begin and end at scheduled times; late entry or reentry is permitted only under exceptional circumstances.

• Mobile phones and non-essential electronic devices must be turned off unless explicitly authorized for instructional use.

• Students must maintain professional conduct, appropriate attire, and respectful engagement in all academic settings.

• Failure to meet engagement expectations may affect a student’s ability to satisfy course requirements in accordance with Section 11 and institutional policy.

• All assessments must be submitted through the designated course platform in accordance with specified instructions.

• Submissions must be:

  • complete

  • correctly formatted

  • uploaded to the correct submission location

• Only submissions successfully recorded in the official system are recognized for grading purposes.

• All assessment deadlines are defined within the course schedule and interpreted according to the official system time of the Learning Management System (LMS).

• System-generated timestamps constitute the official record for submission timing.

• Late submissions are subject to the following conditions:

  • submissions received after the official deadline incur penalties as defined in Section 11 (Grading)

  • submissions not received within the defined assessment availability period are not accepted and are assigned a grade of zero

• Late submission conditions are applied consistently and in accordance with institutional policy.

• Students are responsible for verifying successful submission of all assessments.

• The most recent submission recorded in the system prior to the deadline is considered the official submission.

• Submissions uploaded after the deadline are subject to late submission conditions regardless of prior versions.

Submitted files must be:

• readable

• complete

• in the required format

Unreadable, corrupted, incomplete, or incorrectly submitted files are treated as non-submissions.

Requests for extensions must be:

• submitted prior to the assessment deadline

• supported by valid justification

• processed in accordance with institutional policy

Extensions are not granted through informal or unilateral arrangements and must comply with institutional policy.

• Students are responsible for ensuring that submissions are successfully recorded in the official system.

• Technical issues must be:

  • reported promptly through institutional support services

  • verifiable through institutional systems

• Only verified system-level technical disruptions are considered grounds for deadline adjustments in accordance with institutional policy.

• All submitted work must comply with the requirements of Section 12 (Academic Integrity and Artificial Intelligence Use).

• Submissions must represent the student’s own work and adhere to all stated assessment conditions.

• Failure to comply may result in grade penalties in accordance with Section 11 and institutional policy.

• Where specified in the course assessment structure:

• completion of designated assessment components is required to pass the course

• Failure to complete required assessments may result in failure of the course regardless of overall average.

• This course and its syllabus are governed by institutional policy and applicable accreditation standards.

• All course components, including instruction, assessment, grading, communication, and student support, are subject to institutional policy, which supersedes all course-level materials and communications.

The syllabus establishes the official structure and requirements of the course, including:

• course learning outcomes

• assessment structure and grading criteria as defined in Section 11

• academic integrity requirements as defined in Section 12

• accessibility provisions as defined in Section 13

• communication and operational procedures as defined in Section 14

• submission conditions as defined in Section 15

These elements constitute the authoritative framework of the course.

Adjustments to the course may be made to support effective instruction and academic continuity, including:

• scheduling refinements

• updates to readings, materials, or instructional methods

• modifications required by institutional or accreditation requirements

• adjustments due to verified disruptions affecting course delivery

All adjustments must:

• be communicated through official channels

• comply with institutional policy

• maintain alignment with course learning outcomes

• preserve fairness and consistency in student evaluation

The following elements are not subject to unilateral modification at the course level:

• the grading structure, component weights, and evaluation framework defined in Section 11

• academic integrity requirements defined in Section 12

• institutional accessibility requirements defined in Section 13

• institutional policies governing communication, submission, and academic conduct

Any changes to these elements must be authorized in accordance with institutional policy.

No course modification may be applied retroactively in a manner that:

• alters grading criteria after assessment submission

• changes evaluation standards already applied

• disadvantages students based on previously established requirements

Students are evaluated based on the requirements in effect at the time of submission.

• All approved course adjustments must be:

  • communicated through official channels as defined in Section 14

  • documented and traceable within the course record

• Students are responsible for reviewing and complying with all communicated updates.

• The most recent officially communicated version governs, provided that such updates comply with Sections 11–15 and are not applied retroactively in violation of Section 16.5.

All course revisions must preserve:

• academic standards

• assessment integrity

• fairness and consistency in grading

• compliance with institutional policy and accreditation requirements

No course modification may compromise these principles.

The University defines academic credit in accordance with:

• federal regulation (34 C.F.R. § 600.2)

• institutional policy governing credit-hour equivalency

One (1) credit hour represents a minimum of 45 hours of verified student academic work aligned with defined learning outcomes.

Credit is awarded based on:

• documented, auditable, and verifiable academic activity

• demonstrated achievement of learning outcomes

Time alone does not constitute credit without evidence of learning and assessment.

Credit hour requirements are satisfied through documented and verifiable academic work consisting of:

• direct faculty-led instructional interaction

• independent academic preparation

• faculty-directed experiential or applied learning, where applicable

This standard is equivalent to:

• one hour of classroom or direct faculty instruction

• and a minimum of two hours of out-of-class student work

• per week over an academic term, or the equivalent across alternative academic calendars.

Under no circumstances may variations in scheduling, delivery, format, or instructional design reduce the minimum required workload.

Academic workload is classified into the following categories, which are functionally distinct and not interchangeable:

Academic Engagement Time (AET)

Faculty-Directed Instructional Interaction

• AET consists of structured instructional engagement in which faculty exercise direct academic control.

• AET is organized into the following functional categories:

  • Direct Instruction and Guided Learning

  • Faculty-Moderated Academic Discourse

  • Structured Academic Consultation, Critique, and Feedback

  • Instructional Evaluation and Academic Judgment

  • Faculty-Supervised Applied Instruction

• These categories are functional and non-exhaustive. Any activity qualifies as AET only if it is:

  • faculty-directed

  • required

  • instructionally substantive and academically evaluable

  • aligned with learning outcomes

  • documented and verifiable

• Passive, optional, or undocumented activities do not qualify.

Academic Preparation Time (APT)

Independent Academic Preparation

• APT consists of independent academic work performed outside faculty-directed instruction.

• APT is organized into the following functional categories:

  • Reading, Review, and Study

  • Independent Practice and Problem-Solving

  • Research and Scholarly Inquiry

  • Writing and Academic Production

  • Independent Assignment and Project Development

  • Technical, Analytical, and Applied Independent Work

  • Independent Media-Based Preparation

• These categories are functional and non-exhaustive. Any activity qualifies as APT only if it is:

  • required

  • aligned with learning outcomes

  • demonstrable and measurable through assessment

• APT does not include faculty-directed instructional interaction.

Experiential and Individual Study Time (EIT) (where applicable)

Faculty-Directed Experiential and Individualized Learning

• EIT consists of faculty-directed, supervised, and evaluated academic work conducted outside traditional instructional settings.

• EIT is organized into the following functional categories:

  • Field-Based and Applied Learning

  • Independent Study and Faculty-Supervised Research

  • Project-Based and Culminating Academic Work

  • Performance and Presentation-Based Academic Activity

• These categories are functional and non-exhaustive. Any activity qualifies as EIT only if it is:

  • faculty-directed

  • directly supervised or formally overseen by faculty

  • time-defined

  • evaluated and documented

• Unstructured or unsupervised activity does not qualify.

Course workload is determined as follows:

• AET + APT for structured instructional formats

• EIT, where applicable, for experiential or individualized formats

In all cases, courses must meet or exceed the minimum total workload required per credit hour.

Credit hour requirements are invariant across:

• delivery modality (on-campus, online, hybrid)

• academic calendar structure (standard, accelerated, or compressed)

No variation in modality or calendar structure may reduce required workload.

Courses must include regular and substantive interaction between faculty and students.

Such interaction must be:

• academically substantive

• ongoing and structured

• aligned with course learning outcomes

• documented and verifiable through institutional systems

All academic activities contributing to credit must be:

• documented through course records

• verifiable through institutional systems

• subject to academic and accreditation review

Evidence includes, but is not limited to, the following formally recorded sources:

• instructional records

• assignment submissions (Section 15)

• participation and communication records (Section 14)

• graded assessments (Section 11)

All workload components must directly support defined course learning outcomes.

Credit is awarded only where both conditions are satisfied:

• workload requirements are met in full

• and learning outcomes are demonstrably achieved through assessed academic work

This syllabus constitutes the officially approved version of the course document for the specified offering of the course.

• Version: 5.0

• Effective Date: September 3, 2025

This syllabus serves as the authoritative course document for students enrolled in the course. It defines the principal features of the course, including its academic purpose, learning outcomes, assessment structure, instructional expectations, and governing academic conditions. It also serves as a formal reference for instructors, academic administrators, internal and external reviewers, and others with legitimate academic or institutional responsibility for the course. As such, the syllabus supports clarity, consistency, academic quality, and institutional accountability in the design, delivery, evaluation, and review of the course.