The Fung institute has funded the following courses in an effort to provide suitable technical courses for Master of Engineering students. These are just a handful of courses available for Master of Engineering students. For a full list of courses offered at Berkeley each semester, check out the online schedule of classes.
Biomedical Device Development Engineering design is the process by which an idea is generated, developed, constructed, tested, and managed. Typical bioengineering courses often focus on idea conception and construction. True engineering design integrates not only these two essential elements, but also the process of evaluating, planning, and testing a product. This course highlights the context and value of product development: the formalized process bridging the gap between device proof of concept and an FDA approved biomedical product in the marketplace. Instructor led lectures and student led case studies will form the core of the coursework.
This course teaches concepts and skills required to design, prototype, and fabricate interactive devices — that is, physical objects that intelligently respond to user input and enable new types of interactions. The first half of the semester will be dedicated to a survey of relevant techniques in 3D modeling and fabrication; electronics and circuit board design; sensing and actuation for interaction; embedded software development, wired and wireless communication with mobile devices, computers, and networks; and user interface programming. In the second half of the semester, students will propose and carry out a significant design project of their own choice in groups. We encourage students to work on projects in health technologies (a CITRIS focus area); PhD students can also explore projects related to their area of research after consultation with us. Class sessions will alternate between concepts and experiential learning.
IEOR 240 Optimization Analytics
Computing technology has advanced to the point that commonly available tools can be used to solve practical decision problems and optimize real-world systems quickly and efficiently. This course will focus on the understanding and use of such tools, to model and solve complex real-world business problems, to analyze the impact of changing data and relaxing assumptions on these decisions, and to understand the risks associated with particular decisions and outcomes. Course topics will include techniques such as linear, integer, non-linear and multistage programming, and metaheuristics, applied to problems in planning, logistics, finance, energy and other domains.
IEOR 241 Risk Modeling, Simulation, and Data Analysis
This is an exclusive Masters of Engineering course, in which students will develop a fundamental understanding of how randomness and uncertainty are the root causes of risk in modern enterprises. The technical material will be presented in the context of engineering team system design and operations decisions case studies.
Students will learn techniques for measuring and controlling risk that are critical in designing and managing robust, large-scale, complex global systems. Student teams will use this technology to:
• Learn the strengths and weaknesses of different approaches, giving them a foundation for selecting methodologies and software that are appropriate for different classes of problems.
• Learn how to model random processes and experiment with simulated systems.
• Be introduced to the different technologies used to develop make decisions where there is uncertainty.
• Understand various human decision-making approaches: intuition, analysis, consensus, guessing, etc.
• Become critical consumers of the management sciences – learn to question authority.
• Learn to communicate their ideas and solutions effectively in written reports.
• The course is a mixture of modeling art, analytical science, and computational technology.
IEOR 251 Supply Chain Innovation, Strategy and Analytics
This course introduces you to the field of Supply Chain Management through a series of lectures and case studies that emphasize innovative concepts in Supply Chain Management that have proven to be beneficial for a good number of adopters. Innovations that we will discuss include collaborative forecasting, social media, online procurement and technologies such as RFID.
Along the way, you will learn the foundations of Supply Chain Management and about supply chain issues that arise in a wide variety of industries, from healthcare to consumer goods retailing (both bricks & mortar and e-commerce) to semiconductor manufacturing. We will link supply chain strategy to corporate strategy and see that, in many cases, the firm’s supply chain strategy is the core of the firm’s business model. To supplement the foundations and strategic aspects, you will also gain exposure to analytical models that can be helpful in assessing the benefits and costs of various innovative supply chain concepts. In summary, the course will integrate innovation, strategy and analytics in the context of Supply Chain Management.
Most class sessions will start with a discussion of a case that you have been asked to prepare. Typically, this will be followed by a lecture / discussion on material that will provide the foundation for the next week’s case.
IEOR 252 Service Operations Management
This course focuses on the design of service businesses such as commercial banks, hospitals, airline companies, call centers, restaurants, Internet auction websites, and information providers. The material covered in the course includes internet auctions, procurement, service facility location, service quality management, capacity planning, airline ticket pricing, financial plan design, pricing of digital goods, call center management, service competition, revenue management in queueing systems, information intermediaries, and health care. We will primarily focus on the economic perspective of these subjects, and will largely take the game theoretic approach to tackle problems.
The goal of the instructors is to equip the students with sufficient technical background to be able to do research in this area. Students who have not advanced to MS, MS/PhD or PhD levels or are not in the IEOR department must consult the instructor before taking this course for credits.
ME 246 Energy Conversion Principles
Covers the fundamental principles of energy conversion processes, followed by development of theoretical and computational tools that can be used to analyze energy conversion processes. Also introduces the use of modern computational methods to model energy conversion performance characteristics of devices and systems. Performance features, sources of inefficiencies, and optimal design strategies are explored for a variety of applications.
ME C231A Experiential Advanced Control Systems Design LAB
Experience-based learning in the design of SISO and MIMO feedback controllers for linear systems. The student will master skills needed to apply linear control design and analysis tools to classical and modern control problems. In particular, the participant will be exposed to and develop expertise in two key control design technologies: frequency-domain control synthesis and time-domain optimization-based approach.
ME 201 Modeling and Simulation of Advanced Manufacturing Processes
Many modern manufacturing methods involve a series of events to process a material in order to obtain characteristics that the raw material does not possess. These processes are wide-ranging and broadly include:
• heat treatment: casting, annealing, etc,
• working of materials: forging, rolling, drawing, bending, etc,
• material removal: cutting (including laser processing), drilling, chemicalmechanical
polishing (CMP), abrasion, chemical etching, etc
• spraying: coating, epitaxy, chemical-vapor deposition (CVD) and
• functionalization of materials: adding particulates and fibers (composites),
powder metallurgy, etc, and optimization of materials.
This course provides the student with an introduction to basic industrial processes, modeling techniques and computational methods to treat classical and cutting edge manufacturing processes in a coherent and self-consistent manner
ME 290R Topics in Manufacturing : Advanced Manufacturing Systems
This course is designed to prepare students for technical leadership in industry. The objective is to provide insight and understanding on the main concepts and practices involved in analyzing, managing manufacturing systems for high quality, cost effective and sustainable manufacturing.
The objective of this course is to ensure that our students:
a. Gain solid foundations on the analysis of Advanced Manufacturing Systems Analysis (AMS), including concepts, frameworks and methodologies.
b. Understand and apply sustainable engineering practices.
c. Put into practice decision-making activities based on solid academic rigor, quantitative tools and simulation models oriented for AMS
d. Align their AMS to a company’s strategy to deliver business advantage.
The impact of this class on the Mechanical Engineering program includes delivering core production concepts and advanced skills that blend vision and advanced manufacturing elements. This course is highly recommended for students on the Sustainable Engineering track in Mechanical Engineering.
201A Introduction to Probability at an Advanced Level
Students will receive no credit for 201A after taking 200A. Six hours of lecture and three hours of laboratory for seven weeks. Prerequisites: Multivariable calculus, one semester of linear algebra, and Statistics 134 or consent of instructor. Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables. (F) Staff
201B Introduction to Statistics at an Advanced Level
Students will receive no credit for 201B after taking 200B. Six hours of lecture and three hours of laboratory for seven weeks. Prerequisites: Statistics 200A, Statistics 201A, or consent of instructor. Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory.
230A Linear Models
Three hours of lecture and two hours of laboratory per week. Prerequisites: Matrix algebra, a year of calculus, two semesters of upper division or graduate probability and statistics. Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares.
232 Experimental Design
Three hours of lecture and two hours of laboratory per week. Prerequisites: 200B or equivalent. Randomization, blocking, factorial design, confounding, fractional replication, response surface methodology, optimal design. Applications.