USC Schedule of Classes

A combination of plant tours, laboratory experiences, and lecture are used to introduce the philosophy, subject matter, aims, goals, and techniques of industrial and systems engineering.

Engineering examples are used as a platform to introduce computer programming skills for problem solving; critical thinking skills.

Techniques for modeling uncertainty: discrete and continuous random variables, probability distributions, expectations and conditional probability; Poisson processes, Markov chains and queuing decision models.

Managing a project intended to create an engineered system. Methods, processes, tools. Lessons-learned from actual practice. Case studies from real projects. Presentations by student teams.

Modeling, solution analysis and applications of complex decision problems using linear, dynamic and integer optimization.

Engineering project team formation and team dynamics including organizational behavior, psychology and business management, all in the context of technology development; decision-making and negotiation.

Role of humans in technological systems; human-systems integration; ergonomics; anthropometry and workstation design; work methods and measurement; human error, safety and accident analysis.

Data models for industry applications. Modeling and designing robust databases. Implementing and querying databases with SQL. Innovations in database applications.

Production, resource and service planning, forecasting, scheduling, vendor and location selection, inventory; integrated decision systems for the control of production, services and supply chains.

Modeling of complex discrete event systems, input and output analysis, system evaluation via experimentation on the simulation model, applications to industrial and systems engineering problems.

Design, modeling and analysis of complex systems, challenges they present and methods for resolving those challenges. Impact of technology on work and organizational design.

Basic principles that govern the dynamic socio-technical interactions of open technology innovations on competitive global markets, emphasizing real-world practices to uncover blue-ocean innovation opportunities.

Essentials of human factors and computer interface for the design, development, implementation, and evaluation of integrated media systems.

Methodology to store, organize, cleanse, harmonize and stage enterprise data for analytics. Report, visualize, slice and dice, forecast and predict trends.

Individual research and readings. Not available for graduate credit.

Investigate client organization operations, evaluate alternative designs to meet client needs, complete preliminary design review.

Develop the detailed design including countermeasures for risks, complete critical design review, implement the project plan, close-out the project.

Framing engineering management situations with statistical methods. Experiments, distributions, regression, ANOVA, hypothesis, introduction to Bayesian analysis.

Fundamental accounting principles, financial analysis and financial control, strategic management and a review of alternative project delivery methods.

Strategies to improve hospital operations, including supply chains, staffing, informatics, data analytics and patient flow.

Dependencies among long, intermediate and short-range planning and scheduling of production systems considering objectives, technological constraints and solution algorithms.

Methods, processes and tools for managing projects to create engineered systems. Project management; engineering economics; risk management. Industry-drawn large-scale engineering case studies. Team project presentations.

Planning data collection to investigate relationships between product/process design choices (materials, temperatures, etc.) and performance, empirical modeling to predict performance, identification of best design choices.

Principles of quality management, quality philosophies and frameworks, role of leadership and strategic planning to improve quality, process management, measurements to improve quality and performance.

Supervised learning. Linear regression, cross validation, ridge and lasso regression, logistic regression, k-nearest-neighbors, decision trees, random forest and gradient-boosting models, support vector machines, neural networks.

Fundamentals of Linear Programming, Integer Programming, quadratic and nonlinear programming for analytics and data science problems.

Consulting project concepts, frameworks, analytical tools and managerial skills with a focus on the use of data analytics, design thinking and insight-driven frameworks.

Data preprocessing, data cleaning, data summarization, dimension reduction, data visualization, anomaly detection, classification and clustering. Modeling dependencies using association rules.

Linear programming models for resource allocation; simplex and revised simplex methods; duality; sensitivity; transportation problems; selected extensions to large scale, multiobjective and special structured models.

Modern portfolio theory and optimal decision-making problems in finance. Market microstructure and high-frequency trading. Machine learning models for quantitative finance.

Stochastic processes: conditional expectation, Poisson process, discrete time Markov chains, continuous time Markov chains with queueing applications, non-Markovian queues and reliability.

Methods and algorithms for automated text analysis; machine learning; predictive web data analytics; information retrieval; social media data; natural language documents and graphs.

Design and management of engineering teams. Group decision-making, motivation, leadership, infrastructure requirements, performance measurement, team diversity, conflict and integration.

Principles and practices of technology development and implementation, with application to products and systems in manufacturing and services.

Modern developments in data representation/analysis, nonconvex optimization, high-dimensional probability, sketching, clustering, low-rank models, inverse problems, applications in imaging, machine learning, computer vision and neuroscience.

Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.

Introduction to technologies and disciplines used to collect, integrate, clean and otherwise prepare data into a well-structured, controlled, documented and understood analytic-ready dataset.

Explores fundamental techniques such as recursion, Fourier transform ordering, dynamic programming for efficient algorithm construction. Examples include arithmetic, algebraic, graph, pattern matching, sorting, searching algorithms.

Economic evaluations of engineering systems for both government and private industry; quantitative techniques for evaluating non-monetary consequences; formal treatment of risk and uncertainty.

Decisions under uncertainty; utility theory; multiattribute utility; probability elicitation; conjugate distributions; decision analysis applications, framing, modeling using spreadsheets or software; cognitive biases, behavioral decision theory.

Concepts underlying the economic analysis of engineering projects; applications to call and put options; utility theory and mathematical optimizations models; simulation.

Analytical methods in an organizational context. Improving organizational performance. Tools for diagnosing, measuring, analyzing, improving and managing performance.

Statistical methods for building intelligent and adaptive systems that improve performance from experiences; Focus on theoretical understanding of these methods and their computational implications. Recommended preparation: Undergraduate level training or coursework in linear algebra, multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required.

Psychological systems and physiological characteristics of humans; how they impact engineering design of technological systems and human-computer interactions.

Introduction to modeling and analysis of stochastic systems, with an emphasis on discrete-event simulation of non-Markovian systems.

The role enterprise resource planning systems (ERPs) play in organization; integrating and coordinating the many business processes such as demand planning, procurement, production and sales.

Examine principles, strategies, practices and case studies where new technology and innovation are transforming the traditional business paradigms to identify best practices in real-world implementation.

Concepts of risk analysis, risk in engineered systems, environmental risk, security risks; fault trees, event trees, risk simulations; risks and decision-making.

Research leading to the master's degree; maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Thesis.

Course content will be selected each semester to reflect current trends and developments in the field of industrial and systems engineering.

Methodologies of planning, conducting, analyzing and interpreting controlled physical or computational experiments for causal inference with broad data science and engineering applications.

Stochastic processes, conditional expectation, Poisson processes, renewal processes, regenerative processes, and discrete and continuous time Markov chains.

Understanding optimization problems with constraints and extensions to the treatment of problems involving nonlinearity, uncertainty, discrete variables, logical constraints, multi-agent non-cooperative games and hierarchical problems.

Current research, guest speakers in the field; review papers; guidance in preparing research proposals and special projects. Duplicates credit in the former ISE-650abc. Open only to Fifth Year Seniors and Master’s Students.

Fundamental techniques for design and analysis of algorithms. Dynamic programming; network flows; theory of NP-completeness; linear programming; approximation, randomized, and online algorithms; basic cryptography. Prerequisite: CSCI 570. Recommended preparation: familiarity with algorithms and discrete mathematics. Open only to doctoral students.

Research leading to the doctorate. Maximum units which may be applied to the degree to be determined by the department. Graded CR/NC.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.

Credit on acceptance of Dissertation.