Course:FRE521E/Syllabus
COURSE INFORMATION
Session and term: 2024W2
Class location: MCML 154
Class times: Mon and Wed 15:00 – 16:30
Lab times: N/A
Course duration: Jan 6 - Feb 14
Credits: 1.5
COURSE DESCRIPTION
This course introduces modern machine learning methods in R through the lens of applied economics, with a focus on climate, food, environmental, and resource applications. Students learn why and when machine learning can outperform traditional econometric approaches—particularly for prediction-focused problems, high-dimensional datasets, and cases where relationships are too complex for standard functional forms.
Through assignments and a final project, students will have opportunities to code, experiment with the machine learning models, and communicate their modelling choices and findings in clear, accessible language best suited for non-technical audience.
Real-world applications include:
- Using satellite imagery and high-dimensional data to predict crop yields and classify land cover
- Using spatial and sensor data to predict environmental outcomes such as air quality
- Using large government and customs datasets to predict trade volumes and trade flows
INSTRUCTOR
Instructor: Dr. Kevin Laughren
Email: kevin_laughren@sfu.ca
Office hours: Tuesdays/ Thursdays 12:00- 1:00 PM ( in-person, immediately after class) Room: TBD
LEARNING OUTCOMES
By the end of this course, students will be able to:
- Identify when an applied economics problem could benefit from a machine learning approach (e.g., prediction problems, large amounts of unstructured data, modelling high-dimensional relationships without restrictive functional forms)
- Describe the trade-offs associated with machine learning approaches versus classic econometric approaches
- Apply modern machine learning methods such as random forests, boosted models, and neural networks to generate economic insight
- Communicate economic insights using the outputs of machine learning models
ASSESSMENTS
| Learning Assessment Activity | Percentage | Dates |
| Participation | 10% | Weekly |
| Assignments (best three) | 45% | End of Weeks 2, 3, 4, and 6 |
| Project Outline | 10% | End of Week 5 |
| Final Project | 35% | End of Week 7 |
| Total | 100% |
Learning Materials
Allaire, J. (2012). RStudio: integrated development environment for R. Boston, MA, 770(394), 165-171. Installation of most-recent version is available at https://posit.co/download/rstudio-desktop/
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2023). An Introduction to Statistical Learning with Applications in R. 2nd Edition (corrected printing). Springer. https://www.statlearning.com/
The software and textbook above will be used every week. Readings that are specific to a single lesson or assignment will be posted to Canvas.
COURSE SPECIFIC POLICIES
Writing Exams
All exams will be in-person and will follow MFRE exam protocol (See Student Portal). Exams may be online, e.g., in Canvas, but students must be physically present, use the lock-down browser, and invigilated. If a student is unable to write an exam, they must have a verifiable doctor’s note and must contact the Course Instructor, Course Assistant, and MFRE Program Manager before the scheduled exam date/time. If the documentation is considered legitimate, the student will be informed of the how to proceed.
Exam Policies
Students must complete the final exam. If you are unable to make the final exam, please contact the instructor to make alternative arrangements.
Late and/or Substandard Format Assignments:
Students must complete all assignments. If you are unable to complete an assignment, please contact the instructor to make alternative arrangements.
Group Work and Peer Review
Students will choose their own groups for the two assignments, with a maximum of three students per group. The groups for the two assignments may differ.
COURSE SCHEDULE
| Week | Topic | Readings | Due |
| 1 | Introduction | James et al., Ch. 1-3; Mullainathan & Spiess (2017) | |
| 2 | Trees | James et al., Ch. 8.1 | Assignment 1 |
| 3 | Random Forests | James et al., Ch. 8.2-8.4; Chen et al., (2021) | Assignment 2 |
| 4 | Boosted Models | Storm et al., (2020) | Assignment 3 |
| 5 | Neural Networks | James et al., (2023) Ch. 10; Cao et al., (2012) | Project Outline |
| 6 | Unsupervised Learning: PCA | James et al., (2023) Ch. 6.3, 12.1-12.2 | Assignment 4 |
| 7 | Adaptive Sampling | Russo et al., (2019); Kang et. al, (2016) | Final Project |
Assignments
All assignments, exercises, and your project are required to include both your code (in .r or .rmd format) and a write-up (in .html or .pdf format) that is written and formatted to a professional standard. For assignments where you do not use data provided by the instructor, you will also need to submit a data file (in .csv format). The code should be written so that it loads the relevant data file and compiles on an external computer. You are encouraged to have a peer test that your code compiles on their computer before submitting.
Unexcused late assignments are penalized 25% of the grade weight immediately after the deadline, and an additional 25% penalty applies for additional day an assignment is late. For example, assignments that are fifteen minutes late lose 25%, assignments that are one day and fifteen minutes late lose 50%.
Participation
Participation points are awarded for attendance (3.5%), accurate completion of in-class exercises (3.5%), and instructor discretion, awarded based on participating in classroom discussion and collaborating with peers during exercises and assignments (3.0%)
MFRE PROGRAM - COURSE PROTOCOL POLICIES
Recordings
There is no required distribution of recordings of class. Recording will be provided based upon on the decision of the course instructor. Classes are designed as and are intended to be in-person.
Copyright
All materials of this course (course handouts, lecture slides, assessments, course readings, etc.) are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.
Missing Classes/Labs
Students are expected to attend all classes, labs, or workshops. If you cannot make it to a class, lab, or workshop due to a medical or personal emergency, email your Instructor, your Course Assistant, and Olivier Ntwali, MFRE Program Coordinator ahead of time to let them know.
Respectfulness in the Classroom
Students are expected to be respectful of their colleagues at all times, including faculty, staff and peers. This means being attentive and conscious of words and actions and their impact on others, listening to people with an open mind, treating all MFRE community members equally and understanding diversity.
Respect for Equity, Diversity, and Inclusion
The MFRE Program strives to promote an intellectual community that is enhanced by diversity along various dimensions including status as a First Nation, Métis, Inuit, or Indigenous person, race, ethnicity, gender identity, sexual orientation, religion, political beliefs, social class, and/or disability. It is expected that all students and members of our community conduct themselves with empathy and respect for others.
Centre for Accessibility
The Centre for Accessibility (CfA) facilitates disability-related accommodations and programming initiatives designed to remove barriers for students with disabilities and ongoing medical conditions. If you are registered with the CfA and are eligible for exam accommodations, it is your responsibility to let Olivier Ntwali, Academic Program Coordinator, and each of your Course Instructors know. You should book your exam writing with the CFA using its exam reservation system: for midterm exams or quizzes, at least 7 days in advance; and final exams, 7 days before the start of the formal exam period.
MFRE PROGRAM - ACADEMIC HONESTY POLICIES
Plagiarism and Academic Dishonesty
Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.
Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:
- Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
- Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
- Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
- Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
- Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
- Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.
Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non‐acceptable conduct for examinations and graded assignments as presented via MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.
Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.