DATA 311: Machine Learning
Winter Term 1, 2025
Course Information
Instructor
Name: Dr. Irene Vrbik1 (she/her)
email: irene.vrbik@ubc.ca
Office Hour: Mon 2:30 p.m - 3:30 p.m
Office: SCI 104
Course TAs
Pantea Fatemi
Nima Eslami
Class Schedule
Location: ART Floor 1 - Room 103
Time: Mon Wed | 11:00 a.m. - 12:30 p.m.
Course Description
Official Calendar Description
DATA_O 311 (3) Machine Learning: Regression, classification, resampling, model selection and validation, fundamental properties of matrices, dimension reduction, tree-based methods, unsupervised learning. [3-2-0].
Prerequisites
Either (a) one of STAT 205, STAT 230 or (b) a score more than 75% in one of APSC 254, BIOL 202, PSYO 373; and one of COSC 111, APSC 177.
Course Structure
The course will be made up of 3 hours of lecture per week plus 2 hours of weekly laboratory (labs).
Lecture Format
Lectures will be given in-person. Slide decks will be posted https://irene.vrbik.ok.ubc.ca/data311/ prior to our scheduled lecture time. Slides might be supplemented with handwritten material which I will upload to Canvas after lecture. Lectures may also include discussions which you will only gain access to by attending lectures. While statistical software (i.e. R code and output) will be discussed during lecture, practical skills and applications of topics are covered primarily in computer labs.
Lab format
All students must be registered for a lab (held weekly unless otherwise specified). Please check your registration to determine your lab section and time. Labs are structured as walk-though tutorials which help to develop the practical skills of performing machine learning in R. You may work through the lab material on your own time and/or work through them during your scheduled lab. To ensure that TAs are not overloaded during a single lab, please do not attend labs for which you are not registered.
Labs sessions will be hosted by your TA in persons. While they are primarily there to provide guidance on carrying out analyses in R, they additionally provide the opportunity to meet other students from class, ask questions and/or discuss concepts from lecture, and receive assistance on assignments. Thus, labs will act as addition “office hours” held by your TAs. While labs are not mandatory (i.e. attendance will not be taken) you are highly encouraged to attend. Do not skip going through this material as lab content will be fair game for testing on midterms and the final exam.
Course Objectives
Course Overview
The course is designed to introduce students to classical machine learning methods for regression and classification with an emphasis on model validation (i.e. it is not enough to fit a model, students should be able to estimate how good the resulting model is). By taking this course, students will gain experience in applying machine learning algorithms in R and develop skills for effectively communicating a proper interpretation of the results.
Learning Outcomes
At the end of this course, students should be able to:
- build a model and validate it
- understand fundamental proofs for techniques that rely on matrix algebra
- compute linear regression and apply hypothesis testing
- perform logistic regression and discriminant analysis
- apply the K-fold cross-validation methods
- apply the LASSO and ridge regression methods
- apply bagging and boosting on tree-based methods
- apply some methods of unsupervised learning (e.g. principal components, or k-means clustering).
- manipulate data sets in R including applying the above methods
- create reproducible documents that embed R code, text, figures, and more.
Course Schedule
Find our tentative and up-to-date schedule at: https://irene.vrbik.ok.ubc.ca/data311/
Marking and Evaluation
| Grade Item | Percentage of Grade |
|---|---|
| iClicker | 5% |
| Assignments | 15% |
| Midterm 1 (Wednesday, Oct 15) | 20% |
| Midterm 2 (Wednesday, Nov 19) | 20% |
| Final Exam (Date TBD) | 40% |
Final grades will be based on the evaluations listed in Table 1. The final grades will be assigned according to the standardized grading system outlined in the UBC Okanagan Calendar. Faculties, departments, and schools reserve the right to scale grades in order to maintain equity among sections and conformity to University, faculty, department, or school norms. Students should therefore note that an unofficial grade given by an instructor might be changed by the faculty, department, or school. Grades are not official until they appear on a student’s academic record.
Passing Criterion
To pass the course, a student must:
- Achieve a final grade of 50% or higher, as determined by the weighting scheme table and
- Obtain a passing grade (50% or more) on at least one of the in-person assessments (midterm 1, midterm 2 or final exam).
Note: If a student does not meet criterion 2, their final grade will be capped at 45, regardless of their overall score.
Learning Activities
Class participation will involve answering in-class clicker questions to reinforce key concepts and encourage engagement.
Learning Materials
We will be using UBCO’s Learning Management System (LMS) Canvas: https://canvas.ubc.ca/. It is recommended that you log in daily to check for announcements, participate in discussions, access and submit assignments, and review upcoming deadlines. Students are expected to check Canvas regularly for updates. You may also review and adjust your notification preferences for this course (refer to the supporting documentation for instructions).
Textbook
There are no required textbooks for this course. A suggested reference is An Introduction to Statistical Learning (2nd edition) Gareth et al. (2013), which is freely available online at https://www.statlearning.com/. This text provides additional examples and explanations that complement the course material. Some additional content will be available from (Hastie 2009).
Software
Students will need access to a computer capable of running R (see https://www.r-project.org/) and RStudio (see https://posit.co/products/open-source/rstudio/), which will be used for assignments and data analysis throughout the course. Both R and RStudio are free to download and install.
Other Course Policies
Missed Activity Policy
Midterms
There will be two (2) synchronous closed-book midterms given during the term, held in class on Wednesday, Oct 15 and Wednesday, Nov 19. The midterms are not cumulative; the second midterm will cover only the material presented after the cutoff for the first midterm. Specific details on the material covered in each midterm will be provided closer to the dates and may vary depending on the pace of the course.
If a sufficient excuse is provided (e.g., a medical condition supported by documentation from a doctor), the weight of a missed midterm will be transferred to the final exam. No make-up tests will be offered.
Final Exam
The examination period begins Monday, December 8 and ends Friday, December 19. The comprehensive final exam is cumulative, covering all the material presented throughout the course. The final exam will be in-person and closed-book.
Except in the case of examination clashes and hardships (three or more formal examinations scheduled within a 24-hour period) or unforeseen events, students will be permitted to apply for out-of-time final examinations only if they are representing the University, the province, or the country in a competition or performance; serving in the Canadian military; observing a religious rite; working to support themselves or their family; or caring for a family member. Unforeseen events include (but may not be limited to) the following: ill health or other personal challenges that arise during a term and changes in the requirements of an ongoing job. Further information on Academic Concession can be found under Policies and Regulation in the Okanagan Academic Calendar (see Academic Concession)
Assignments
There will be approximately four (4) assignments. Assignments will incorporate material covered during lab as well as lecture. Answers will be submitted electronically through Canvas. Students will be required to create and submit a fully reproducible document using Quarto (.qmd).
Assignments must be submitted electronically through Canvas. Late assignments will incur a 10% deduction for each day (including weekends) past the due date. Assignments more than 2 days (48 hours) late will not be accepted and will receive a grade of
Important Dates
Please note these important Dates and Deadlines:
- Start: Tuesday, September 2
- Finish: Friday, December 5
- Midterm Break: November 10 - 14
- Teaching Days: 62
- Exams Start: Monday, December 8
- Exams Finish: Friday, December 19
There will be no class, office hours, or labs during the Midterm Break nor on the following Statutory holidays:
- Tuesday, September 30: National Day for Truth and Reconciliation
- Monday, October 13: Thanksgiving Day
If you observe any other holidays not listed above, please feel free to contact me directly if you believe they may conflict with the outlined course structure.
Expectations
Your responsibilities to this class, and your education as a whole, include regular attendance and active participation. You are responsible for helping to create a classroom environment where everyone can learn. At a basic level, this means respecting your classmates and the instructor, and treating them with the courtesy you expect to receive in return.
Inappropriate classroom behavior includes, but is not limited to:
- Disrupting the classroom atmosphere
- Engaging in non-class activities
- Talking on a cell phone
- Inappropriate use of profanity during discussions
- Using abusive or disrespectful language toward the instructor, other students, or about individuals or groups
UBC Values
UBC creates an exceptional learning environment that fosters global citizenship, advances a civil and sustainable society, and supports outstanding research to serve the people of British Columbia, Canada, and the world. UBC’s core values are excellence, integrity, respect, academic freedom, and accountability.
Policies and Regulations
Visit UBC Okanagan’s Academic Calendar for a list of campus-wide regulations and policies, as well as term dates and deadlines.
Resources to Support Student Success
Visit the Student Support and Resources page to find one-on-one help or explore resources to support your experience at UBC Okanagan, as well as many other campus services available to all students.
About the Academic Integrity Matters (AIM) Program
AIM is a program that provides help and supports with academic integrity (AI) issues for undergraduate and graduate students. Please contact aim.ok@ubc.ca for any questions or for a 1-on-1 appointment with an AIM consultant.
Academic Misconduct
Academic integrity is integral to UBC as an institution of higher learning and research. Violations of academic integrity (i.e., academic misconduct) harm the academic enterprise; as a result, serious consequences arise and penalties may be imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment or exam and more serious consequences may apply if the matter is referred for consideration for academic discipline. Careful records are kept to monitor and prevent recurrences. Any instance of cheating or taking credit for someone else’s work, whether intentionally or unintentionally, can and often will result in at minimum a grade of zero for the assignment, and these cases will be reported to the Head of the Department and Associate Dean Academic of the Faculty.
A more detailed description of academic integrity, including the University’s policies and procedures, may be found in the Academic Calendar.
Use of AI Tools
Artificial Intelligence (AI) tools (such as ChatGPT, Copilot, or other large language models) can be valuable resources for learning. In this course:
✅ Permitted uses: You may use AI tools to clarify concepts, brainstorm ideas, or debug code for your own understanding. You must verify any outputs independently, as these tools can generate errors or misleading results.
🚫 Not permitted: Submitting AI-generated solutions (text, code, or analysis) as your own work is considered academic misconduct. This includes using AI to complete assignments, projects, or exams without substantial modification and understanding.
📝 Transparency: If you use AI tools in a way that informs your submission (e.g., helping brainstorm code structure), you must acknowledge this in your work (e.g., “Used ChatGPT to clarify the syntax of an R function”).
🎯 Purpose: The goal of assignments is for you to practice and demonstrate the skills taught in class. Overreliance on AI tools will limit your learning and can harm your performance on quizzes, exams, and interviews.
Learn more through the Generative AI website.
A Note on Collaboration
While collaboration with peers is encouraged, submitting work that you do not fully understand or cannot explain will be considered a violation of academic integrity. Any form of academic dishonesty, including plagiarism or unapproved sharing of work, will be handled according to UBC’s academic integrity policies.
Academic Misconduct
Violations of academic integrity (i.e., academic misconduct) lead to the breakdown of the academic enterprise, and therefore serious consequences arise and harsh sanctions are imposed. For example, incidences of plagiarism or cheating may result in a mark of zero on the assignment or exam and more serious consequences may apply if the matter is referred for consideration for academic discipline. Careful records are kept to monitor and prevent recurrences. Any instance of cheating or taking credit for someone else’s work, whether intentionally or unintentionally, can and often will result in at minimum a grade of zero for the assignment, and these cases will be reported to the Head of the Department and Associate Dean Academic of the Faculty.
Grievances and Complaints Procedures
A student who has a complaint related to this course should attempt to resolve the matter with the instructor first. Students may talk first to someone other than the instructor if they do not feel, for whatever reason, that they can directly approach the instructor. If the complaint is not resolved to the student’s satisfaction, the student should e-mail the Department Head Dr. Ramon Lawrence (ramon.lawrence@ubc.ca).
Student Service Resources
CMPS MATH/STAT/DATA Help Center
CMPS MATH/STAT/DATA Help Center is a free resource for students in MATH, STAT, and DATA courses. Many of the tutors have taken these courses themselves, so you’ll be able to find someone who can help. No appointment is needed—just drop by anytime during the hours below! You’re welcome to come on your own or bring a group of friends to work together. The Help Center opens September 8.
Location: SCI 396
Hours (Term 1):
- Monday: 12:00 PM – 4:00 PM
- Tuesday–Thursday: 12:00 PM – 7:00 PM
- Friday: 12:00 PM – 2:30 PM
A schedule for individual tutors will be posted on the door of SCI 396.
Disability Resource Centre
The Disability Resource Centre (DRC) facilitates disability-related accommodations and programming initiatives to that ameliorate barriers for students with disabilities and/or ongoing medical conditions. If you require academic accommodations to achieve the objectives of a course please contact the DRC at:
UNC 215 250.807.8053
Email: drc.questions@ubc.ca
Web: www.students.ok.ubc.ca/drc
Equity and Inclusion Office
Through leadership, vision, and collaborative action, the Equity & Inclusion Office (EIO) develops action strategies in support of efforts to embed equity and inclusion in the daily operations across the campus. The EIO provides education and training from cultivating respectful, inclusive spaces and communities to understanding unconscious/implicit bias and its operation within in campus environments. UBC Policy 3 prohibits discrimination and harassment on the basis of BC’s Human Rights Code. If you require assistance related to an issue of equity, educational programs, discrimination or harassment please contact the EIO.
UNC 325H 250.807.9291
Email: equity.ubco@ubc.ca
Web: www.equity.ok.ubc.ca/
Office of the Ombudperson
The Office of the Ombudsperson for Students is an independent, confidential and impartial resource to ensure students are treated fairly. The Ombuds Office helps students navigate campus-related fairness concerns. They work with UBC community members individually and at the systemic level to ensure students are treated fairly and can learn, work and live in a fair, equitable and respectful environment. Ombuds helps students gain clarity on UBC policies and procedures, explore options, identify next steps, recommend resources, plan strategies and receive objective feedback to promote constructive problem solving. If you require assistance, please feel free to reach out for more information or to arrange an appointment.
UNC 328 250.807.9818
Email: ombuds.office.ok@ubc.ca
Web: www.ombudsoffice.ubc.ca/
Student Learning Hub
The Student Learning Hub is your go-to resource for free math, science, writing, and language learning support. The Hub welcomes undergraduate students from all disciplines and year levels to access a range of supports that include tutoring in math, sciences, languages, and writing, as well as help with academic integrity, study skills and learning strategies. Students are encouraged to visit often and early to build the skills, strategies and behaviors that are essential to being a confident and independent learner. For more information, please visit the Hub’s website.
LIB 237 250.807.8491
Email: learning.hub@ubc.ca
Web: www.students.ok.ubc.ca/slh
Sexual Violence Prevention and Response Office (SVPRO)
The Sexual Violence Prevention and Response Office (SVPRO) is a confidential place for those who have been impacted by any form of sexual or gender-based violence, harassment, or harm, regardless of where or when it took place. SVPRO aims to be a safer space for all UBC students, faculty, and staff by respecting each person’s unique and multiple identities and experiences. All genders and sexualities are welcome.
Nicola Townhome 120, 1270 International Mews 250.807.8053
Email: svpro@okangan@ubc.ca
Web: www.svpro.ok.ubc.ca/
Wellbeing and Accessibility Services (WAS)
Wellbeing and Accessibility Services (WAS) supports holistic student wellbeing in body, mind, and spirit. Students can access nurses, physicians and counsellors for health care and counselling related to physical health, emotional/mental health and sexual/reproductive health concerns. WAS is also home to the Disability Resource Centre, Spiritual and Multi-Faith Services, and Campus Health and Education. If you require assistance with your health, please contact Wellbeing and Accessibility Services for more information or to book an appointment.
UNC 337 250.807.9270
Email: healthwellness.okanagan@ubc.ca
Web: www.students.ok.ubc.ca/was
Independent Investigations Office
If you or someone you know has experienced sexual assault or some other form of sexual misconduct by a UBC community member and you want the Independent Investigations Office (IIO) at UBC to investigate, please contact the IIO. Investigations are conducted in a trauma informed, confidential and respectful manner in accordance with the principles of procedural fairness. You can report your experience directly to the IIOby calling 604-827-2060.
Web: https://investigationsoffice.ubc.ca/
E-mail: director.of.investigations@ubc.ca
Safewalk
Download the UBC SAFE – Okanagan app. Don’t want to walk alone at night? Not too sure how to get somewhere on campus? Call Safewalk at 250.807.9270. For more information visit: https://security.ok.ubc.ca/safewalk/
References
Footnotes
see how to pronounce my name on my website: https://irene.vrbik.ok.ubc.ca/about/↩︎