Fall 2024 Supplement and Course Offerings
This page was last updated on: April 11, 2024
When information is updated or added, it will be highlighted.
Academic Calendar 2024-25
Fall 2024 Registration Deadlines
Olin Schedule of Deadlines
| Session | Add | Drop + Pass/No Credit | Withdraw |
|---|---|---|---|
|
Full Semester Sept 3 - Dec 12 |
September 16, 2024 |
November 7, 2024 |
December 12, 2024 |
|
Session I Sept 3 - Oct 21 |
September 9, 2024 |
October 4, 2024 |
October 21, 2024 |
|
Session II Oct 22 - Dec 12 |
October 28, 2024 |
November 7, 2024 |
December 12, 2024 |
Cross-Registration Deadlines and Instructions
Instructions for using the Olin portal to cross-register are here.
All deadlines follow the academic calendar of the HOST school.
For deadlines, refer to the links below:
Click HERE for Cross-Registration FAQ and Instructions (scroll down to Helpful Links for Cross Registration and Cross Registration FAQ)
Important Notes about Fall 2024 Cross Registration Dates & Deadlines:
- You may begin submitting cross-registration requests at any time; however, Babson and Wellesley will not begin accepting requests from Olin until April 22 (see the BOW Academic Calendar). Cross-registration requests are initially routed through the Olin Registrar's Office, which will forward them to Wellesley and Babson on April 22. You can anticipate a response from the other colleges a few days after April 22. This is because all of the BOW schools give their own students first dibs at registering for their classes, then offer remaining open seats for cross registration.
- Babson's Add & Drop deadlines are early this fall! Babson's Add and Drop deadlines are both August 30. Babson professors strongly prefer their students to attend classes beginning with the first meeting (or no later than the 2nd meeting), and Babson is starting classes on August 26. Also, if you drop a class at Babson after August 30 you will receive a grade of WD, so be sure that you want to take a particular class before enrolling.
- Wellesley introductory classes: Wellesley first-year students enroll in their fall classes over the summer. Wellesley will not admit cross-registered students into introductory level classes prior to August 30 because they are holding those seats for their own first-year students. If you would like to request an introductory level class at Wellesley, be prepared that they may initially deny your request and tell you to check back after August 30 to see if there are any openings.
You may have a maximum of one active cross-registration class per school at a time. If your initial request is denied, you can submit a new request.
Dropping Cross-Registered Courses: Drop the course with the Registrar’s Office of the host institution by their deadline AND inform the Olin Registrar’s Office. Dropping the course from your Olin schedule via my.olin.edu WILL NOT inform the host school and you will still be considered registered for the course, so don’t do it! If you do not drop the course with the host school in a timely manner, you may end up with a “W” or an “F” on your transcript. If you have any questions regarding this process, please email registrar@olin.edu. Drop deadlines are posted on the BOW website.
Questions? Contact the Registrar’s Office at Olin College, registrar@olin.edu
Registration Times
FA24 Registration for returning students takes place April 16th- 19th, 2024.
(Rising) Seniors (class of 2025) and FA24 Completers (time adjusted to accommodate AWAY students)
- Group 1: 4/16 @ 12pm – 4/17 @ 12pm
- Group 2: 4/16 @ 2pm – 4/17 @ 2pm
(Rising) Juniors (class of 2026)
- Group 1: 4/17 @ 5:30pm – 4/18 @ 3pm
- Group 2: 4/17 @ 9pm – 4/18 @ 3pm
(Rising) Sophomores (class of 2027)
- Group 1: 4/18 @ 5:30pm – 4/19 @ 3pm
- Group 2: 4/18 @ 9pm – 4/19 @ 3pm
First Year Students (Class of 2028)
Students are pre-registered by the Registrar's Office staff closer to the start of the fall semester. The April 16-19 registration period is for returning students only.
Registration times are also available via your portal MyStAR login – registration from left frame of My StAR > Registration, Add/Drop.
Important Registration Notes
Catalog Change: ODEs/physics requirements:
Beginning in 2023-24, the mathematics and physics requirements will change. Details for this change can be found HERE.
Class of 2025 CAPSTONE Registration:
You may have or will soon receive notification from the capstone team if you have been assigned to ADE, SCOPE or TVC. The Registrar’s Office will register you for your assignment. If you have questions about your assignment, please connect with Scott Harris (TVC), Sarah Bloomer (SCOPE), and/or Ben Linder (ADE). If you did not receive your allocation email, please reach out to the capstone team as soon as possible.
Curriculum Category in the Offerings List (pdf):
The curriculum category in our Course Offerings List will help you know what the offering typically corresponds to for specific degree requirements. This column should also help Engineering degree students with flexible concentrations understand the generalized topic track of a particular course. Additionally, sometimes these categories change as Olin changes so be sure to reference them and to inquire if you have questions. Use these as a guide. Use the catalog for further information either in degree requirements or via the course description.
Course Schedule Blocks:
Course blocks are 100-minutes, with 10 minutes between blocks and a common one-hour lunch block for the Olin Community! Blocks between 8:30am to 5:30pm are on Monday/Thursday, Tuesday/Friday patterns; Evening blocks, 6pm- 8:40pm are on Monday/Wednesday and Tuesday/Thursday patterns.
Prerequisite Waivers:
If you are given permission to waive a course pre-requisite, you must forward the approval email to registrar@olin .edu so the waiver can be added to your student record. If the waiver is not added to your record prior to registration, the system will prevent you from registering! It is important to take this step well BEFORE registration opens.
Waitlists for Courses with Two Numbers:
If you want to join a waitlist for a course with two numbers, please email registrar@olin.edu after you register. We will maintain a waitlist as the system does not allow waitlists for courses with two numbers.
The two courses are:
- MTH2137_and_ENGR3537: Machine Learning
- MTH2188A_and_SCI2099A: Decision Making in Sustainable Systems
Cross-Listed Courses:
Cross-listing is a term associated with two distinct course numbers for a single academic activity. The activity can be defined under two topics depending on what aspect of the course content a student focuses on during their enrollment. To this end, the student elects the path at the beginning of the course (no later than the last day to add) by selecting the appropriate course number. The distinction is important because it could frame your project and impact how your experience works toward completing a requirement.
There are no Cross-Listed courses in Fall 2024.
Experimental Grading (EG):
The ‘EG’ grade represents an “Experimental Grade” designation, implemented in a small number of courses during a curricular experiment that began in 2009. Each student may undertake no more than one “EG” course per semester. An ‘EG’ grade in a student’s transcript indicates that a student completed the course’s learning objectives and received instructor feedback based upon criteria that do not have direct mapping onto the ABCDF grading system. Students who do not complete the learning objectives will receive a “no credit” designation on their transcript (similar to the “no credit” option for pass/no credit courses).
There are two courses being offered with experimental grading in Fall 2024:
- ENGR3299: Special Topics in Design: Mixed Methods Product Evaluation
- ENGR4599: Tech Venture Capstone
Thesis Research Option:
A reminder for students and advisers that Olin has a year-long Thesis Research Option available to students working with faculty mentors. The program provides an opportunity for students to conduct advanced research work over a duration of 2 consecutive semesters that culminates in a written thesis document. Enrollment in the thesis option is by faculty mentor approval. Students would register for an ISR-G: “Thesis Research” in Semester 1, and ISR-G: “Thesis” in Semester 2, for 4 credits per semester.
Semester Course Schedule List + Grid
Degree requirements and course requisites are outlined in the Course Catalog. Course descriptions can also be found in the catalog and portal Course Search. Sometimes these categories change as Olin changes so be sure to reference them and to inquire if you have questions. Use these as a guide. Use the catalog for further information (information can be found in degree requirements or in specific course descriptions).
Notes on Courses: New, Special Topics, or Updated Information
Instructor: Linder, Ben
Credits: 4ENGR Hours: 2-4-6
Recommended Requisite: Experience with computer-aided design and digital fabrication and appreciation for and knowledge of aesthetics are desirable. Experience with sketching, model making, welding, torch cutting, plasma cutting, angle grinding, metal casting, and glass blowing is also helpful.
Course Description: This intermediate design elective explores fabrication through a collaboration with the Fine Arts 3D and Industrial Design departments at MassArt. Students from all three disciplines will work together using the MassArt metal foundry and hot glass shop in combination with CAD modeling and 3D printing as tools for experimentation and production exploring their creative potential. Emphasis is on the development of new processes that combine iterative form development with digital fabrication technologies and studio production techniques. Students will meet at MassArt on Wednesdays from 3:00-8:00 PM. Transportation costs will be covered by the course, and students do not need access to a car to participate, although it helps. Can be used in E:Design concentrations (not Design Depth).
Instructors: Student Led w/ advisor Chris Lee
Credits: 2 ENGR Hours: 3.3-0-2.7
Recommended Requisite: Mechanics of Solids and Structures
Course Description: Mechanical Analysis (MechAnalysis) is a 2 ENGR Credit opportunity that expands upon existing mechanical engineering concepts in the curriculum, introduces practical analysis methods not currently covered, and will provide a more concrete toolkit for analyzing engineering problems encountered throughout a student’s Olin education and early career.
Instructors: Dean, Victoria; Shuman, David and Rogers, Claire
Credits: 4 ENGR Hours: 4-0-8
Pre-requisites: One of the following: ENGR2360 or (ENGR2355 and ENGR2365 – the previous 2 cr versions), MTH2131, MTH2135, MTH2188A OR ENGR3525
Course Description: This impact-centered learning experience is aimed at improving Olin’s building automation and HVAC systems through projects around fault detection and energy efficiency. Throughout the year, groups will work on sub-projects with different topics/goals, including visualization and interpretation of campus data, machine learning on campus data, thermal modeling of campus buildings, software infrastructure to get data on and off of the HVAC control servers, and more. We will collaborate with Franklin Cummings Tech Prof. John Terasconi and some students from FCTech’s Building Energy Management program.
Instructor: Bloomer, Sarah
Credits: 4 ENGR Hours: 4-0-8
Pre-requisite: Collaborative Design ENGR2250
Course Description: For students interested in product research, product strategy, UX research or UX design. This course provides hands-on experience with qualitative and quantitative methods and brings in practitioners as speakers. In 2024 projects will be sourced beforehand to make sure we can get enough users to engage in meaningful research.
Overall, students learn:
• Write user research test plans – using both qualitative and quantitative methods
• Implement test plans
• Analyze results and make recommendations
• Communicate those results; practice creating clear communications
• Work with real companies to evaluate their products or services
• Speakers: Industry practitioners and leading UX practitioners
There are two types of UX research: Generative and Evaluative. Evaluation is fundamental to designing successful products. Targeting existing product, evaluation answers the question: can people use the thing (app, site, pmthroduct, service etc). Today’s product design teams capture both qualitative and quantitative data are often used together to inform ongoing product design and product strategy.
This course focuses on evaluating existing products to answer questions such as:
• How easy is the product to learn and use?
• Where are the problem areas in the product design?
• Do the features work as expected? Do they work in the context of use?
• Do they work for all target users?
• How might I improve the user experience of an existing product?
Instructor: Barrett, David
Credits: 4 ENGR Hours: 4-0-8
Pre-requisite: ENGR2360: Intro to Thermal-Fluid Systems or ENGR2365: Intro Transport or ENGR3310: Transport Phenomena or ENGR3365: Intermediate Transport
Course Description: Computational fluid dynamics (CFD) aims to analyze and solve fluid dynamics problems within a variety of practical contexts. In particular, CFD approximately solves the mathematical models in fluid mechanics using numerical solution of the Navier-Stokes equation with computer modeling software.
In this new course you will use CFD modeling software to leverage the computational power of your computer, completing calculations that would otherwise be impossible to do by hand. You will also generate clear visual representations of your solutions to both understand the flow around and to optimize the fluid design of a particular device or vehicle. To ensure that your solutions accurately represent reality, we will start by re-solving classically complete fluid dynamics problems with CFD tools and cross-check both against validated experimental data.
The course will involve one substantial individually chosen CFD final project.
Instructor: Goenka, Chhavi
Credits: 4 Hours: 4-0-8
Pre-requisite: ESA (Signals)
Recommended Requisite: Coding (either MATLAB or Python, but MATLAB preferred).
Course Description: Imaging, imaging algorithms and imaging systems are being used everyday to analyze and interact with the world around us, from facial recognition to medical data collection, from search & rescue to surveillance, from autonomous vehicles to assistive devices. In this course, we will learn about the basic concepts of image processing, image reconstruction from incomplete data and image analysis to obtain meaningful information from imaging data. We will also study how and where there is possibility of biases being introduced into the entire imaging process – from acquisition to interpretation.
The specific topics (as they apply to imaging) that we will cover include but are not limited to sampling, linear transformation, geometric transformation, convolution, change detection, edge detection, quantization, filtering, compression, color spaces, image segmentation, image reconstruction, classification, feature extraction.
Instructor: Matsumoto, Steve
Credits: 2ENGR Hours: 2-0-4
Pre-requisite: ENGR 2510 (Software Design) or instructor permission
Course Description: Cybersecurity is fundamental to every software application or system, however small, and anyone responsible for maintaining or building any computer system will be accountable for some part of its security. We use systems, software, and tools every day that may be vulnerable to security breaches and incidents. Despite this, cybersecurity remains an afterthought in the design of software, and is often underexplored by those not passionate about the field.
This course aims to provide an introduction to core aspects of cybersecurity that are accessible to, and important for, anyone in a computing-related occupation. Specifically, this course teaches students to:
1. Articulate and implement fundamental security features in the context of a general software application.
2. Explain fundamental cybersecurity concepts and their relation to the broader context of cybersecurity and privacy.
3. Describe and demonstrate both technical and non-technical approaches to security in everyday life.
The course will cover a variety of topics including (but not limited to) the security mindset, writing secure applications and mitigating vulnerabilities, threat modeling, data breaches, authentication, access control, data security, social engineering, and ethics of cybersecurity decisions. While a portion of the class requires work with web applications, all required background information will be provided. Students will be assessed through quizzes, written and programming assignments, and a course project.
Instructor: Student Led w/ advisor Steve Matsumoto
Credits: 4
Note: This is a Student Led class
Pre-requisite: ENGR3525 Software Systems or permission of teaching team
Course Description: This course is an advanced system-level software class intended to explore topics beyond what is normally covered in ENGR3525 Software Systems including networking, concurrency, parallelism, and device drivers. Unlike Software Systems however, this class will be taught in Rust, a modern and quickly growing programming language widely cited to be well-considered that introduces new ways to reason about writing safe and efficient software programs.
While proficiency in writing Rust programs is an expected learning outcome of this course, of greater importance is the ability to apply the underlying theories in order to write more robust programs in any software context you may be working on.
Instructor: Taylor, Orion
Credits: 4 Hours: 4-0-8
Recommended Requisites: QEA1, QEA2, QEA3 and ESA
Course Description: This course will be a mix of the standard advanced math topics for engineering students, including (but not limited to): numerical methods, ordinary and partial differential equations, optimization, algorithms, nonlinear dynamics, and linear algebra. Coursework will be split up into 1-2 week modules, which will be in the form of either mini-projects or problem sets (these will be the primary form of assessment). Class will be a combination of lecture and solo/group work time.
Instructor: Huang, Jean
Credits: 4 SCI
In this survey course we learn fundamental principles of biology in a journey through the field from the molecular to systems levels. We examine different classes of biological problems and interactions across multiple scales through reading and discussion of primary and secondary literature from the field. We will discuss examples from the environment, microbiology, and current events and technologies and examine the intersection between biology and society. We will gain an understanding of core principles of biology, which will enable us to better understand and develop solutions to complex problems. Projects include examination of biology in the context of systems and exploration of ways in which biology informs interdisciplinary problem solving. Through projects and work in the laboratory students develop a practical and foundational understanding of biological principles and practice.
Instructor: Pratt, Joanne
Credits: 4 Hours:
Recommended Requisite: Some prior Biology experience (Olin foundation course, AP/IB Bio, research experience or permission of the instructor)
Course Description: This course explores the intricate relationship between the environment and human health. Students will investigate how various environmental factors such as air and water quality, climate change, pollution, and chemical exposures impact human health both in the present and future. Through discussions, activities and laboratory exercises, students will gain an understanding of the mechanisms by which environmental factors affect health, and explore potential solutions and adaptations.
Instructor(s): Michalka, Sam and Shuman, David
Credits: 2 ENGR + 2 MTH Hours: 3-0-9
Prerequisites: Software Design, QEA1, and QEA2; OR linear algebra, calculus, and Python coding experience (if the latter, contact the instructor to request a prerequisite waiver).
Registration notes: Fulfills ProbStat requirement
Course Description: Machine learning technology is rapidly reshaping how we live our lives. Machine learning approaches have driven recent progress in an array of technologies that have the potential to realize huge positive impacts on our world (e.g., self-driving cars, language translation, personalized recommendation and search). However, the influence of machine learning does not end with these highly visible technologies. Machine learning algorithms are impacting our world in ways that are far less known to the general public, such as in job applicant evaluation, criminal justice, finance, politics, and medicine. The principal aim of this course is to equip students with a multi-faceted and interdisciplinary skillset to understand, implement, and critically evaluate machine learning systems. In service of this goal, students will learn the major algorithmic and mathematical frameworks that undergird modern machine learning methods. Students will learn effective processes for implementing, testing, and refining machine learning systems across a range of application domains. Students will learn how the decisions that machine learning practitioners make interact with larger societal context by considering factors such as transparency, fairness, bias, and privacy. The course assignments will take a variety of forms, including problem sets, openended projects, and discussion-oriented readings. Whether you want to develop new machine learning algorithms, apply machine learning to real world problems, or simply want to have a better understanding of what’s happening in this rapidly evolving field, this course has something of value to offer you.
Instructor: Wood, Alison
Credits: 2 MTH + 2 SCI Hours: 4-0-8
Registration notes: Fulfills ProbStat requirement. Students must register for both course numbers.
Course Description: This class will introduce you to a variety of quantitative decision-making systems and metrics, such as benefit-cost analysis and risk assessment, to supplement the technical and entrepreneurial decision-making tools you learn elsewhere throughout our curriculum. You’ll also learn about ways that our purely quantitative decision tools fall short of representing the real world and when they are still useful (“all models are wrong…”), and we’ll explore some of the reasons that decision-making is so complicated. Decision-making content will be situated in the context of complex systems, and we’ll return, throughout, to the decision objective of reaching sustainable outcomes (for contextually appropriate definitions of “sustainable”). The semester will center on how we can ultimately make decisions in a world of tradeoffs, taking advantage of widely used tools along with recognition of systemic complexity, context, and the messiness of human nature.