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Online MBA Curriculum

Through MSOE’s integrated curriculum, you will develop leadership competencies using a combination of core content and field projects. Courses are focused on strategic business functions, organizational leadership and the effective application of business knowledge. Our strategic MBA content includes the analysis of leading-edge business cases and requires you to apply your learning to solve real problems in your business or organization. In addition to the core required MBA courses, further hone your skills with a specialization of your choosing in analytics, new product management or education leadership.

The online MBA is 37 credits in total, with 19 credits from the required courses and 18 credits from your specialization courses.

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Online MBA Required Courses

BUS 5700 Marketing (3 credits)

This course introduces you to the major elements of marketing and the marketing planning process. Throughout the term we will concentrate on understanding and addressing stakeholder (customer) needs, implementing voice of customer plans and developing marketing branding and communications strategies and tactics to help you achieve your specific goals.

Students learn the major elements of marketing and the marketing planning process, with a focus on identifying and addressing customer and stakeholder needs. Students design a marketing plan that includes market segmentation, target markets and marketing mix.

BUS 6000 Organizational Behavior (3 credits)

The purpose of this course is to help you examine and understand organizations from numerous and diverse perspectives at the individual, group and organizational levels. Beginning with the deepest foundations of individual values, emotions, motivations and behaviors, you will examine and reflect on self and others to understand, predict, and guide multifaceted group dynamics while connecting this to the complex interplay between organizational structure, culture, and change. Diversity, globalization and ethics will be woven throughout the course. As an advanced course in management, ground in contemporary and classic theory, the course will be conducted in a team-based learning format to promote reflection, context-based application and integrated learning of self and others. The concepts and skills you learn in this course can help you to influence and lead members of groups and organizations, while helping you to reach personal fulfillment today and in the future.

BUS 6100 Ethical Leadership (3 credits)

This course introduces students to important ethical challenges they will face as leaders in business. The class will use two perspectives, the individual and the enterprise. The initial focus of the course will be on developing skills in ethical analyses that can assist managers as they make both individual-level and firm-level decisions about responsible courses of action when duties, loyalties, rules, norms, and interests are in conflict.

On an enterprise level, the class will go on to explore the role business can play in helping to meet global societal needs, whether it involves the environment, improving health, expanding education or eradicating poverty? Is there any responsibility on the part of business to help meet those needs? What are models of successful business engagement in this area? How should success be measured? Are there limits to what businesses can and should do, and what institutional changes will enable businesses and entrepreneurs to better succeed? This course provides students the opportunity to engage in the critical analysis of these and other questions that lie at the foundation of social impact and responsibility.

Students will engage through projects, games, case studies learning through self-discovery and guided class discussions with their peers.

BUS 6151 Data Visualization (3 credits)

Within this course, students learn how to visualize data to support effective decision making. Students will learn how to create visualizations and dashboards for a variety of audiences. Special attention will be paid to storytelling with data, in the context of a business decision/scenario. Students will learn how to evaluate the appropriate human-analytics interfaces to best support communication and interpretation of results at all organizational levels.

BUS 6400 Economics (3 credits)

This course explores the concepts and principles of microeconomics, which govern the functioning of a firm/organization under different market conditions. It further aims at enhancing the understanding capabilities of students about macroeconomic principles and decision making by business and government. Time Value of Money will be used to solve business problems.

Applied Project Management I-IV (1 credit each)

  • BUS 7921 Applied Project Management I (1 credit)
  • BUS 7922 Applied Project Management II (1 credit)
  • BUS 7923 Applied Project Management III (1 credit)
  • BUS 7924 Applied Project Management IV (1 credit)

The Applied Project Management MBA sequence of courses presents the knowledge and skills required to identify, plan, implement, monitor and control, and close projects according to Project Management Body of Knowledge (PMBOK) methodology. Through authentic project leadership experiences, students build and manage teams, assign tasks and hold others accountable for project deliverables. They also study failed projects with an eye toward drafting recommendations for future project and team success. Finally, the course describes the roles and responsibilities of not only project leaders, but also team members and organization steering committees.

Online MBA General Track Courses

BUS 5500 Statistics and Data Analytics (3 credits)

This course reviews and applies the foundational concepts of statistics and analytics to common problems and scenarios found in the business domain. The focus on descriptive and diagnostic statistical techniques helps prepare students for more advanced study of prescriptive and predictive analytics. Topics include interval estimation, hypothesis testing, parametric and nonparametric tests and an introduction to advanced analytics. The use of statistical programming languages such as R and Python will be emphasized to expose the student to contemporary statistical processing environments on computing clusters.

BUS 5800 Innovation and Entrepreneurship (3 credits)

You have a solution to a problem; how do you turn it into a successful business model? What are the tools and techniques for taking an idea to market? This course will expose students to the most successful processes and tools for the intrapreneur ( innovating and commercializing a product, service or process idea in an existing organization) and the entrepreneur (developing a product or service and creating a start-up). Students will develop a business model using a visual tool and a series of hypotheses formulations to determine product-market fit as the foundation of a successful business opportunity. The business model becomes the outline for a pitch—a primary tool of the entrepreneurially minded value-creator. This class will result in an abbreviated business plan; ready for detailed analysis and launch.

BUS 6200 Finance and Accounting (3 credits)

In this course, students cover key financial concepts, identify the financial decisions confronting organizations, and learn to analyze an organization’s status using financial tools and techniques. It includes acquiring a knowledge of and working with the financial statement as well as the principal aspects of financial planning and control. Program and organizational budgets and the budget process are included, as well as the evaluation of spending proposals. Students also learn the role of financial planning in an organization’s strategic planning process, including cash flow presentation and analysis, capital investment decision making and both long-term and short-term financial decision making.

BUS 6500 Supply Chain Management (3 credits)

This course focuses on supply management strategies and supply chain design. We will examine the varied approaches to supply as it relates to product development, sourcing, logistics, coordination efforts, and performance measurements in a global setting. The objectives of this course are to build a basic understanding of supply strategy and supply base management, analyze and study approaches to some of the issues and problems encountered in supply chain management today, and outline potential areas and actions designed for improvements in performance.

BUS 6600 Human Resources Management (3 credits)

This course prepares students to conduct job analysis, write job descriptions and identify job specifications as the foundation for talent management. Students will develop plans to recruit, interview, and screen candidates and also define strategies to onboard, socialize, develop and evaluate people to drive organizational success. In addition, students will identify and evaluate a variety of employee compensation and benefit models and assess the impact on employee performance, motivation, commitment and retention. Finally, the course focuses on diversity, equity, and inclusion from both legal and ethical frameworks.

BUS 6900 Strategic Management (3 credits)

Have you ever wondered what it takes to develop a winning competitive business strategy? How does a firm achieve superior performance in a world where the global pace of change seems to be constantly accelerating? This course embraces the challenges in the context of two specific imperatives—value creation and opportunity recognition. Value creation involves creating and maintaining first-rate performance in the face of dynamic industry shifts and competitive forces. Opportunity recognition requires foresight and decisiveness with a sense of responsibility and stewardship for utilizing the resources available to the firm to maximize potential using their competitive advantage to impact the industry and the world positively.

Online MBA in Advanced Business Strategy Using AI and Analytics Specialization Courses

BUS 5500 Statistics and Data Analytics (3 credits)

This course reviews and applies the foundational concepts of statistics and analytics to common problems and scenarios found in the business domain. The focus on descriptive and diagnostic statistical techniques helps prepare students for more advanced study of prescriptive and predictive analytics. Topics include interval estimation, hypothesis testing, parametric and nonparametric tests and an introduction to advanced analytics. The use of statistical programming languages such as R and Python will be emphasized to expose the student to contemporary statistical processing environments on computing clusters.

BUS 5800 Innovation and Entrepreneurship (3 credits)

You have a solution to a problem; how do you turn it into a successful business model? What are the tools and techniques for taking an idea to market? This course will expose students to the most successful processes and tools for the intrapreneur (innovating and commercializing a product, service or process idea in an existing organization) and the entrepreneur (developing a product or service and creating a start-up). Students will develop a business model using a visual tool and a series of hypotheses formulations to determine product-market fit as the foundation of a successful business opportunity. The business model becomes the outline for a pitch—a primary tool of the entrepreneurially minded value-creator. This class will result in an abbreviated business plan; ready for detailed analysis and launch.

BUS 6121 Data Wrangling and Exploration (3 credits)

This course provides an overview of data-driven decision making and the use of business analytics to support organizational performance. Special attention is paid to identifying appropriate sources of data, evaluating the quality of data, wrangling the data for specific analytical techniques, and exploring patterns within the data. Students will learn current practices, tools, and methods for data wrangling and exploration. Effective interpretation and communication of results are emphasized. The use of programming languages such as R and Python will be emphasized to expose the student to contemporary analytic processing environments on computing clusters.

BUS 6131 Predictive Analytics (3 credits)

Within this course, students learn to identify appropriate tools and/or combinations of tools to address decision-making scenarios within an organization. Special attention is paid to the application of analytics to predict future trends and probabilities. Students will learn the current tools and methods for predictive analytics, as well as the limitations of these methods. Ongoing focus is placed on communicating the interpretation of analytical results to a range of audiences. The use of the Python programming language is emphasized to expose the student to contemporary analytic processing environments on computing clusters. Machine learning and deep learning fundamentals are used to demonstrate the power of statistical learning on prediction in the era of Big Data.

BUS 6141 Analytics Leadership and Strategy (3 credits)

This course introduces the student to the concepts of analytics leadership and strategy to include identification of analytic stage maturity identification and evaluation. Topics include analytics life cycle, the research process, business decision making, building analytic capacity for strategic advantage, the analytics maturity model and DELTA framework, building analytic culture and analytic challenges. The course perspective is from the role of executive leadership driving analytic change within the organization.

BUS 6200 Finance and Accounting (3 credits)

In this course, students cover key financial concepts, identify the financial decisions confronting organizations, and learn to analyze an organization’s status using financial tools and techniques. It includes acquiring a knowledge of and working with the financial statement as well as the principal aspects of financial planning and control. Program and organizational budgets and the budget process are included, as well as the evaluation of spending proposals. Students also learn the role of financial planning in an organization’s strategic planning process, including cash flow presentation and analysis, capital investment decision making and both long-term and short-term financial decision making.

Online MBA in New Product Management Specialization Courses

BUS 5500 Statistics and Data Analytics (3 credits)

This course reviews and applies the foundational concepts of statistics and analytics to common problems and scenarios found in the business domain. The focus on descriptive and diagnostic statistical techniques helps prepare students for more advanced study of prescriptive and predictive analytics. Topics include interval estimation, hypothesis testing, parametric and nonparametric tests and an introduction to advanced analytics. The use of statistical programming languages such as R and Python will be emphasized to expose the student to contemporary statistical processing environments on computing clusters.

BUS 5800 Innovation and Entrepreneurship (3 credits)

You have a solution to a problem; how do you turn it into a successful business model? What are the tools and techniques for taking an idea to market? This course will expose students to the most successful processes and tools for the intrapreneur (innovating and commercializing a product, service or process idea in an existing organization) and the entrepreneur (developing a product or service and creating a start-up). Students will develop a business model using a visual tool and a series of hypotheses formulations to determine product-market fit as the foundation of a successful business opportunity. The business model becomes the outline for a pitch—a primary tool of the entrepreneurially minded value-creator. This class will result in an abbreviated business plan; ready for detailed analysis and launch.

BUS 5950 New Product Management (3 credits)

Why are some firms more successful than others at innovating new products? Why do they win more often? What are the best practices of the most successful product innovation firms? This course will allow you to learn about the best practices of phase-gate process firms and agile and lean product development practitioners. New products (innovations) are physical products, processes, software, as well as services. You will conduct an audit of current practices against a backdrop of best practices and develop a continuous improvement plan for innovation. The emphasis will be on creating a hybrid innovation process that fits with a given firm. We will study the innovation process at leading firms like Toyota, Solar Turbines, Embraer and Ford. The course is structured around professional certification by the Product Development Management Association.

BUS 6200 Finance and Accounting (3 credits)

In this course, students cover key financial concepts, identify the financial decisions confronting organizations, and learn to analyze an organization’s status using financial tools and techniques. It includes acquiring a knowledge of and working with the financial statement as well as the principal aspects of financial planning and control. Program and organizational budgets and the budget process are included, as well as the evaluation of spending proposals. Students also learn the role of financial planning in an organization’s strategic planning process, including cash flow presentation and analysis, capital investment decision making and both long-term and short-term financial decision making.

BUS 6500 Supply Chain Management (3 credits)

This course focuses on supply management strategies and supply chain design. We will examine the varied approaches to supply as it relates to product development, sourcing, logistics, coordination efforts, and performance measurements in a global setting. The objectives of this course are to build a basic understanding of supply strategy and supply base management, analyze and study approaches to some of the issues and problems encountered in supply chain management today, and outline potential areas and actions designed for improvements in performance.

BUS 6900 Strategic Management (3 credits)

Have you ever wondered what it takes to develop a winning competitive business strategy? How does a firm achieve superior performance in a world where the global pace of change seems to be constantly accelerating? This course embraces the challenges in the context of two specific imperatives—value creation and opportunity recognition. Value creation involves creating and maintaining first-rate performance in the face of dynamic industry shifts and competitive forces. Opportunity recognition requires foresight and decisiveness with a sense of responsibility and stewardship for utilizing the resources available to the firm to maximize potential using their competitive advantage to impact the industry and the world positively.

Online MBA in Education Leadership Specialization Courses

BUS 6112 Global Achievement Gap—Education Leadership (3 credits)

Leadership and the Global Achievement Gap engages students in evaluating research and various positions taken related to student achievement, social-emotional learning, and school leadership in relation to the global achievement gap. Focus is placed on reviewing studies that address the K-12 global achievement gap, identifying the extent of the gap, understanding the underlying factors that contribute to the gap and exploring changes in the U.S. educational system and related policies that have the potential to reduce or eliminate the gap while improving educational achievement for all students. Primary topics include evidence-based teaching and learning, high performing team development, work-based learning experiences, social-emotional learning, equity, special education, and improvement tools and processes. Assignments for the course are designed to be practical, applicable, and provide a foundation for students to explore areas of interest related to their program project.

BUS 6202 Finance and Accounting—Education Leadership (3 credits)

In this course, students will cover key financial concepts, identify the financial decisions confronting school districts, and learn to analyze a district’s status using financial tools and techniques. Accounting methods used in the public and private sector, as well as key financial statements are presented. Program and organizational budgets and the budget process are included, as well as the evaluation of spending proposals. Students also learn the role of financial planning in an organization’s strategic planning process.

BUS 6302 Statistical Thinking and Data Analytics—Education Leadership (3 credits)

This course provides an overview of common statistical techniques used in educational research and clinical practice. Students will learn the importance of using statistics and data analysis to support decision-making and reporting. They will understand the importance of using both quantitative data and qualitative data in an educational setting. They will learn to formulate questions, identify legitimate sources of data, and assess data quality.

This course will work on collecting and analyzing data related to MBA/EL culminating project using descriptive statistics, inferential statistics, data analysis, review of research, and survey instruments. Students will also spend time understanding the statistical formulas more deeply around concepts commonly used in education: such as standard deviation, risk ratios, normal distribution, cohort growth and frequency distributions. Embedded support will be given around WISEdash, WI State Report Cards, National Center for Educational Statistics and other locally used data monitoring tools.

BUS 6602 Human Resource Management—Education Leadership (3 credits)

This course prepares students to identify, recruit, select, hire, orient, evaluate and develop the talent needed to drive organizational success in education environments. Current and emerging trends in strategic human resources and the work environment are identified and analyzed. Students identify and evaluate a variety of workforce culture issues and assess the impact of different employee recruitment, performance, engagement and retention strategies. Legal and regulatory requirements affecting the workforce are also reviewed. In addition, students will identify and evaluate a variety of employee compensation and benefit models. Examples, scenarios and applications in this course will focus on K-12 school settings.

BUS 6802 Innovation and Entrepreneurship—Education Leadership (3 credits)

This course helps students design innovative strategies to help their organization realize its greatest potential. Students explore how organizations pursue improvement and innovation, and why some organizations generate poor results. Students will also learn the stages and activities of the innovation in a social sector, and map and manage associated risk.

BUS 6902 Strategy—Education Leadership (3 credits)

This course examines the use of data to communicate organizational priorities and identify core operational processes and strategies for improvement. It also considers how new technologies can equip an organization to achieve its objectives, including the use of process analysis, lean tools and techniques to prioritize and enhance organizational performance. Additionally, this course will explore the value and importance of leadership in and around diversity, equity, inclusion and belonging and understanding the historical, present and future contexts in a wide variety of areas related to social justice and equity.

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Admissions Dates and Deadlines

Aug
1
Priority Deadline
August 1
Fall 2024
Aug
12
Application Deadline
August 12
Fall 2024
Sep
3
Term Start
September 3
Fall 2024

Milwaukee School of Engineering has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.

Required Courses

CSC 5201—Microservices and Cloud Computing (4 Credits)

Course Description

This course introduces the concepts, architectural, and practical techniques needed to design and implement cloud-native microservices. Students will design, implement, deploy, and operate a microservice using widely used technologies and current best practices.

Emphasis will be placed on scalable web and machine learning services. Students will apply what they learn in a series of hands-on lab exercises and complete a final project using a distributed computing platform.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Leverage containerization to package and run software
  • Design and implement a basic microservice that interacts through REST APIs, implements access control, uses distributed storage systems, and is deployed in a containerized form
  • Apply access control patterns using authentication and authorization following security best practices
  • Analyze and identify potential throughput, scalability, reliability, and consistency issues with distributed microservice architectures
  • Create and manage continuous integration and deployment to automate software related to microservices and pipelines
  • Deploy a multi-service distributed application using a container orchestration tool

Prerequisites by Topic

  • Version control/Git
  • Operating systems
  • Databases

Course Topics

  • Virtual machines and containers and associated best practices for writing containerized, stateless applications
  • Microservice architectures and implementation patterns including event-driven architectures and designing and implementing REST APIs
  • Distributed storage systems (e.g., key-value stores, columnar stores, object stores, distributed file systems, and relational databases), tradeoffs between consistency, availability, and partition tolerance, and efficient access patterns
  • Continuous integration and deployment technologies and usage patterns
  • Tooling for orchestration and deployment of multi-service systems
  • Software, Function, Platform, and Infrastructure as service paradigms and implementing systems
  • Discussion of public cloud services and comparisons of their offerings

Coordinator: Dr. RJ Nowling

CSC 5610—AI Tools and Paradigms (4 credits)

Course Description

This course introduces topics, tools, languages, and methods used in modern AI practice. Subjects of study include a survey of tools used to perform analysis, data preparation, and visualization along with the algorithms, concepts, and theory underpinning AI. Exercises and assignments will expose students to Jupyter Notebooks and libraries in the Python ecosystem for data science, machine learning, and AI.

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  • Use a Jupyter notebook environment to write, structure, run, document (with Markdown), and debug Python programs
  • Use Series and Data Frames to load, manipulate, and analyze tabular data sets
  • Leverage appropriate libraries to solve non-trivial data cleaning, preparation, and transformation challenges
  • Employ a plotting library to create simple plots such as histograms, bar or column charts, line plots, and scatter plots including legends, appropriate axis ranges and labels, appropriate font sizes and resolutions
  • Use exploratory data analysis (e.g., summary statistics and visualizations) to characterize data sets and generate hypotheses about the relationships of variables
  • Use a library to train machine learning models and apply models to generate predictions for data
  • Apply and interpret appropriate metrics for evaluating predictions from machine learning models
  • Describe the components of the intelligent agent framework
  • Apply agent and state-based frameworks to solve various problems, such as game playing
  • Provide examples of search problems and techniques for solving them
  • Describe algorithms such as value iteration and q-learning for identifying optimal policies
  • Describe the role of heuristics and describe the trade-offs among completeness, optimality, time complexity, and space complexity
  • Communicate computational results and interpretations in language appropriate for a technical audience
  • Identify ethical implications of potential biases in data sets, algorithms, and applications of AI

Prerequisites by Topic

  • Programming experience

Coordinator: Dr. RJ Nowling

CSC 6605—Machine Learning Production Systems (4 Credits)

Course Description

Students will design, implement, deploy, and operate a machine learning-powered service, including components for data processing, model training, modeling serving, model evaluation, and monitoring. Technologies and design patterns for streaming and batch data processing as well as storage systems will be introduced.

This course builds on and integrates previous course work in offline machine learning and microservices.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Design, implement, deploy, and operate a machine learning-powered RESTful service and supporting backend systems
  • Design and implement data processing systems that use batch and stream processing patterns and technologies for ingesting and processing event data
  • Apply design patterns and data storage systems to design and implement data storage services to support batch and streaming data processing, model training, and model evaluation
  • Create distributed systems to support model training and evaluation
  • Automate and monitor model training, evaluation, deployment, and operation
  • Evaluate and compare architectures of ML-powered Systems

Prerequisites by Topic

  • Cloud computing
  • Micro services
  • Machine learning

Course Topics

  • Distributed batch and streaming data processing platforms and associated programming paradigms
  • Data representation and storage patterns to support batch and streaming data pipelines and services
  • Reliability, consistency, and scalability challenges and solutions for big data systems
  • Patterns for handling model training and evaluation on data that changes or updates over time
  • Tradeoffs between batch and streaming systems in terms of efficiency, computational capabilities, and time until updates are available based on new data
  • Model serving and deployment
  • Monitoring and alerting for services and performance of machine learning model predictions
  • Implementation of systems for common machine learning tasks such as classification/regression and recommendations

Coordinator: Dr. RJ Nowling

CSC 6621—Applied Machine Learning (4 credits)

Course Description

This course introduces concepts of machine learning that explore the study and construction of algorithms that can learn from and make predictions on data.

Topics include conceptual aspects of machine learning, feature engineering, an introduction to deep learning, and applications of machine learning and deep learning. Students will reinforce their learning of machine learning algorithms with hands-on laboratory exercises for development of representative applications.

Course Learning Outcomes

Upon successful completion of this course, you will be able to:

  • Understand the fundamental concepts and application of supervised, unsupervised, semi-supervised, and reinforcement learning
  • Analyze and describe machine learning algorithms including (but not limited to) logistic regression, SVMs, and decision trees
  • Explain the geometric interpretations of linear and non-linear decision boundaries and their relationship to error metrics (e.g., accuracy)
  • Understand the concepts of learning theory, (e.g., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions)
  • Estimate and plot decision boundaries described by trained models
  • Interpret coordinate systems and distinguish between feature and parameter spaces
  • Derive, visualize, apply, and interpret metrics and loss functions to assess the quality of model predictions and fit to data
  • Choose and justify appropriate experimental designs (e.g., test-train split, cross-fold validation) for evaluating machine learning models
  • Encode non-numerical variables in tabular data as numerical features
  • Describe implications of feature representations with respect to linear separability
  • Apply approaches for engineering and evaluating new features from existing
  • Describe the fundamentals of deep learning and what is required to be able to use it
  • Compare various modern deep neural network architectures (e.g., convolutional neural networks (CNNs), encoder-decoders, generative adversarial networks (GANs), or transformers).
  • Describe industry examples where deep learning can be used to solve real-world problems

Prerequisites by Topic

  • Software development
  • Python
  • Jupyter Notebooks

Coordinator: Dr. RJ Nowling

CSC 7901—Machine Learning Capstone (4 Credits)

Course Description

Students work on a significant machine learning project that incorporates knowledge from previous course work. Throughout the project students will be supported by a faculty advisor who will meet regularly for project updates and guidance. The capstone concludes with a final report and presentation to faculty and industry representatives.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Integrate and apply knowledge and skills acquired in previous course work
  • Analyze a complex data science problem and apply principles of machine learning to design, implement, and deploy a solution
  • Effectively communicate the design, implementation, and impacts of the solution
  • Identify and address ethical and governance aspects of the problem and solution

Prerequisites by Topic

Machine learning
AI tools

Coordinator: Dr. Derek Riley

MTH 5810—Mathematical Methods for Machine Learning (4 Credits)

Course Description

This course surveys the essential linear algebra and multivariate calculus required for graduate study in machine learning.

Topics include matrix algebra, real vector spaces, inner product spaces, differentiation and Newton’s method, partial differentiation, the gradient, the chain rule, and optimization of multivariate functions.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Describe linear and affine subsets of three-dimensional space both algebraically and geometrically
  • Interpret the value of a dot product geometrically
  • Interpret functions of two variables via the corresponding surface, level curves, and contour plot
  • Perform elementary operations with matrices including manipulating expressions and equations involving matrices
  • Determine if a matrix is invertible and find its inverse in this case
  • Solve systems of linear equations using matrix methods and technology
  • Express the solutions of a consistent matrix equation in parametric vector form
  • Determine if a subset of n-dimensional space is a subspace
  • Find the matrix of a linear transformation and bases for its kernel and range
  • Show that a real number is an eigenvalue of a matrix with real number entries by finding the corresponding eigenvectors
  • Determine the eigenvalues of a matrix using technology
  • Use the inner product to find the length of a vector and the distance between two vectors
  • Determine if a set of vectors is an orthogonal basis for a subspace of R^n
  • Find the projection of a vector onto a subspace of R^n
  • Find a general linear model by solving the normal equation
  • Find and interpret the singular value decomposition of a matrix using technology
  • Use numerical methods to locate extrema of a single-variable function
  • Perform operations with series representations of the single-variable elementary functions
  • Find first and higher-order partial derivatives of a function
  • Interpret partial derivatives as rates of change in applications
  • Find the total differential of a function of more than one variable, use it to estimate change
  • Estimate error propagation using the total differential
  • Construct the matrix form of the multivariate chain rule and use it to find a derivative
  • Find the gradient of a function and interpret its direction
  • Find directional derivatives of a function and interpret the results
  • Determine the maximum, minimum, and saddle points on a surface
  • Interpret mathematics from machine learning literature

Prerequisites by Topic

Single-variable calculus

Course Topics

  • Geometry in three dimensions: lines, planes, distance, surfaces, vectors, and the dot product
  • Matrix algebra
  • Matrix equations and invertibility
  • Real vector spaces
  • Linear transformations and their matrices
  • Inner products, orthogonality, projection, and applications
  • Eigenvectors and eigenvalues and interpretation of matrix factorizations
  • Review of single-variable differentiation
  • Taylor series of elementary functions
  • Finding critical points analytically and numerically
  • Partial derivatives
  • Gradients, directional derivatives, and the Jacobian
  • Extrema of functions of two variables

Coordinator: Dr. Anthony van Groningen

PHL 6001—AI Ethics and Governance (4 Credits)

Course Description

This course introduces students to some of the central ethical issues in existing and emerging digital technologies, with an emphasis on the ethics of artificial intelligence (AI), as well as how these issues arise in the context of organizational governance and compliance. Students will explore topics in digital and AI ethics with respect to relevant professional areas-such as data science, computer science, software engineering, user experience and design, among others-as well as topics concerning the broader social implications of digital technologies and the ethical challenges they raise.

The goal of the course is to critically engage with these topics and cases interactively, studying both their theoretical, philosophical context and their practical implications, so that students can pursue continued, independent reflection on key issues in digital and AI ethics and apply the guiding ethical principles that emerge to their own professional and personal lives. In addition to addressing these ethical challenges, students will consider how organizations and institutions—corporations, IRBs, and legislative bodies—can or should respond to such challenges through regulation, oversight, or other mechanisms. Students will be encouraged to draw on their own professional experience in assessing key questions and cases in digital and AI ethics.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Demonstrate advanced knowledge of existing ethical issues in digital technologies and AI
  • Anticipate ethical issues arising from emerging digital technologies and developments in AI
  • Identify the philosophical bases for ethical concerns surrounding digital technologies and AI
  • Exhibit familiarity with and understanding of established ethical frameworks, concepts, and principles within ethical theory
  • Synthesize those theoretical frameworks, concepts, and principles and connect them to applied issues, using the theoretical resources to help understand and resolve the applied issues while at the same time scrutinizing the theoretical principles by evaluating their real-world implications
  • Evaluate competing considerations about engineers’ and designers’ moral responsibility for the products they create and services they provide
  • Engage in independent ethical reasoning on novel problems using theoretical and practical ethical resources learned
  • Foster ethical behavior and integrity in their professional and personal lives using the theoretical and practical resources learned
  • Develop concrete proposals for addressing ethical challenges in digital technologies and AI at the level of organizational governance

Course Topics

  • Ethical frameworks, concepts, and principles (consequentialism, deontology, rights, etc.)
  • Ethics of information, part I: privacy and transparency
  • Ethics of information, part II: intellectual property, individual liberties, and human rights
  • Algorithmic bias, “weapons of math destruction,” and social justice
  • The “black box” explainability problem in deep learning AI
  • Ethics of human-AI interaction and impact on social relationships (e.g., anthropomorphic framing in AI and robotics)
  • Digital technologies and human wellbeing: digital media and mental health
  • Digital technologies and democracy: digital media, filtering, misinformation, and political polarization
  • AI and human work: automation, worker displacement, and the meaning of work
  • Ethics of design and user experience: “dark patterns” and user agency
  • Ethics of data capture, digital advertising, and “surveillance capitalism”
  • Ethics of biometric identification
  • Data privacy and regulatory mechanisms: GDPR, HIPAA, IRB regulations, etc.
  • The internet, social media, and the question of legal status (e.g., public utility status, technology or publishing company status, etc.)
  • Artificial moral agency, part I: theory
  • Artificial moral agency, part II: application (e.g., ethics settings in driverless cars, constraints on autonomous weapons systems, etc.)

Coordinator: Dr. Andrew McAninch

Electives

CSC 5601—Theory of Machine Learning (4 Credits)

Course Description

This course provides a broad introduction to machine learning. Theory of machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Topic categories include decision boundaries, optimization, and both supervised and unsupervised methods. Students will apply the theory to implementing and evaluating machine learning algorithms with hands-on, tutorial-oriented laboratory exercises.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Explain and analyze machine learning models and prediction algorithms (e.g., k-nearest neighbors, logistic regression, SVMs, decision trees) in a precise manner
  • Confidence in applying, manipulating, and interpreting linear algebra and calculus concepts within the context of machine learning
  • Implement and validate code for a mathematical model or algorithm given as an equation
  • Explain the geometric and algebraic interpretations of linear and non-linear decision boundaries and their relationship to error metrics (e.g., accuracy)
  • Understand the concepts of learning theory, i.e., what is learnable, bias, variance, overfitting, curse of dimensionality, splitting data for evaluation of model predictions
  • Estimate and plot decision boundaries described by trained models
  • Be able to encode non-numerical variables in tabular data as numerical features
  • Describe implications of feature representations with respect to linear separability
  • Apply approaches for engineering and evaluating new features from existing data
  • Derive, visualize, apply, and interpret loss functions and associated derivatives to assess the quality of model predictions and fit to data
  • Describe and implement model training in terms of naïve and gradient-driven search problems over parameter space
  • Compare and contrast technical requirements and computational efficiency of two unconstrained optimization algorithms (e.g., gradient descent, Newton’s method)

Prerequisites by Topic

  • Linear algebra
  • Python
  • NumPy
  • Functional programming

Coordinator: Dr. John Bukowy

CSC 5611—Deep Learning (4 Credits)

Course Description

This course provides an in-depth introduction to the foundations of deep learning. Students will learn how to architect, train, and evaluate deep neural networks. Students will gain experience with backpropagation, a variety of network structures, and a variety of options for training networks. This course is open to qualified undergraduates.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:

  • Understand the process of backpropagation and its role in training deep networks
  • Implement a basic deep learning network library
  • Apply deep learning to a unique dataset and analyze performance
  • Compare options for training deep neural networks, for example, optimization methods, generalization methods, normalization, initialization methods
  • Apply transfer learning to leverage pre-trained models
  • Compare various modern network architectures, for example, convolutional neural networks (CNNs), encoder-decoders, generative adversarial networks (GANs), or transformers.
  • Emphasis on topics can vary depending on the instructor’s specialties
  • Evaluate training methods and alternative architectures based on model accuracy, constraints, and training performance
  • Discuss the ethical implications of deep neural network applications
  • Compare the performance of the from-scratch deep neural network library implementation to an existing library when applied to a unique Dataset

Prerequisites by Topic

  • Machine learning
  • Python fundamentals

Coordinator: Dr. Josiah Yoder

CSC 5651—Deep Learning in Signal Processing (4 Credits)

Coordinator: Dr. Eric Durant

CSC 5241—GPU Programming (4 Credits)

Coordinator: Dr. Eric Durant

CSC 5980—Topics in Computer Science (Variable Credits)

Course Description

This course allows for study of emerging topics in computer science that are not present in the curriculum. Topics of mutual interest to faculty and students will be explored.

Coordinator: Dr. Derek Riley

CSC 6999—Computer Science Independent Study (Variable Credits)

Course Description

This course allows for study of emerging topics in computer science that are not present in the curriculum. Topics of mutual interest to faculty and students will be explored.

Coordinator: Dr. Derek Riley