These are the main learning objectives:
- Deepen their understanding of machine learning by exploring more advanced and state-of-the-art methods, including support vector machines, ensemble techniques, and neural networks.
- Understand the importance of structured workflows in solving data science problems.
- Gain first exposure to implementing machine learning models from scratch using PyTorch and applying them to real-world datasets.
- Apply machine learning models real-world datasets using the gold-standard library for machine learning, (scikit-learn.
- Critically evaluate and compare the performance of different models, considering trade-offs between accuracy, interpretability, and computational cost.
- Develop and document reproducible data science workflows, including code annotation, version control (GitHub), and final deliverables.
- Engage in collaborative, project-based learning by working effectively within a group to complete lab assignments and a final project.
- Demonstrate self-directed learning and adaptability by tackling tasks beyond the core material and seeking solutions independently.
- Communicate technical findings clearly and effectively, both in written reports and oral presentations.
- Reflect on individual contributions, strengths, and areas for improvement, incorporating peer and self-critique into a growth-oriented learning process.
Each week, you will be expected to review short video lectures and complete assigned readings before coming to class. The videos serve as a brief introduction to the main concepts, which will be expanded upon during the first class of the week through a hands-on Jupyter Notebook walkthrough.
Since Data Science is inherently collaborative, nearly every week will include a group-based lab session. These labs are designed not only to help you practice what you have learned that week, but also to push you out of your comfort zone by encouraging you to learn something new. Machine Learning requires a strong element of self-directed learning, and these sessions will help you build that skill. Both the TA and I will be available during and after class to provide any support you need.
Groups will be formed at the beginning of the semester by balancing skill levels, and you will work with the same group throughout the course.
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Labs (50%): On most weeks, we will have in-class labs or assignments. These are designed to help you either practice the skills being presented in class or explore new material related to the week's theme. These are group assignments, and you are expected to work closely with your group mates to divide and complete the different tasks. The deliverable will be the completed Jupyter Notebook, to be submitted by the group and which will determine the grade of this assignment. Additionally, each member of the team will submit an individual report describing the specific tasks they contributed to, along with a positive critique of themselves, a negative critique, and a brief plan for how they intend to avoid repeating the same mistake in future assignments. Each member must submit this individual report separately in order to receive a grade
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Final projects (30%): The course will culminate in a final project involving work with a dataset of your choice. This is a group assignment, and you will be working with the same group you collaborated with on the lab assignments. As a group, you will submit a GitHub repository containing well-annotated code, a final report summarizing your findings, and a brief slide deck presenting those findings. Additionally, and similar to the lab assignments, each member of the team will submit an individual report describing the specific tasks they contributed to, along with a positive critique of themselves, a negative critique, and a brief plan for how they intend to avoid repeating the same mistake in a similar future situation. Each member must submit this individual report separately in order to receive a grade. This is an open-ended project designed to allow each group to explore a topic of interest from the semester in greater depth. Depending on time availability, each group will present their findings in class.
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Quizzes (20%)
- A. Python Machine Learning with PyTorch and Scikit-Learn, 4th Edition Freely available on O'Reilly with your UVA account.
- B. Introduction to statistical learning Free PDF.
- C. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Free PDF.
- D. Pattern Recognition and Machine Learning Free PDF.
This is the schedule for the core structure of the course. Please be aware that adjustments may occur throughout the semester, as sometimes things do not go exactly as planned. Any changes will be communicated.
Grading Policies: Courses carrying a Data Science subject area use the following grading system: A, A-; B+, B, B-; C+, C, C-; D+, D, D-; F. The symbol W is used when a student officially drops a course before its completion or if the student withdraws from an academic program of the University.
Grading Scale:
- 93-100 A
- 90-92 A-
- 87-89 B+
- 83-86 B
- 80-82 B-
- 77-79 C+
- 73-76 C
- 70-72 C-
- <70 F
Collaboration and Conflict Resolution: Should any conflict arise within your group—for example, uneven workload distribution or communication breakdowns—please first make an effort to address the issue respectfully among your group members. If the problem persists or becomes difficult to resolve, group members should reach out to the instructor or TA as early as possible. Addressing concerns early allows us to work together toward a constructive solution before the issue impacts your work or group dynamic.
Use of Large Language Model tools: The use of Large Language Models (e.g., ChatGPT) is acceptable and even encouraged for the coding aspects of the lab assignments and final project, although with caveats. You need to remember that they may make mistakes, so any code generated by this kind of tools should be carefully checked. On the other hand, using LLM in the writing parts of the assignments and final project is discouraged, as it may hinder your own learning. Finally, their use is strictly prohibited during and for quizzes.
University of Virginia Honor System: By enrolling in this course, all students are expected to uphold the University of Virginia Honor System (https://honor.virginia.edu/course/honor-code). This includes abiding by the Honor Code in all aspects of the course.
Special Needs: The University of Virginia accommodates students with disabilities. Any SCPS student with a disability who needs accommodation (e.g., in arrangements for seating, extended time for examinations, or note-taking, etc.), should contact the Student Disability Access Center (SDAC) and provide them with appropriate medical or psychological documentation of his/her condition. Once accommodations are approved, just follow up with me concerning any logistics and implementation of accommodations. Please try to make accommodations for test-taking at least 14 business days in advance of the date of the test(s). Students with disabilities are encouraged to contact the SDAC: 434-243-5180/Voice, 434-465-6579/Video Phone, 434-243-5188/Fax. Further policies and statements are available at www.virginia.edu/studenthealth/sdac/sdac.html