# DSCC/LING 251/451: Project Interest Survey (M1) **Due:** February 3, 2026 **Submit via:** Google Form (link on course website) This survey helps me understand your interests and background so I can provide better guidance on your term project. Your responses will also help with forming project groups if you're interested in working with others. --- ## Question 1: Project Ideas **Question type:** Long answer (paragraph) **Question text:** What ideas do you have for potential topics for your term project? **Description/Helper text:** It's early in the course, so you may not have an exact research question or methodology in mind. However, you can still identify topic areas or personal interests. For instance: - Are there specific application domains you're interested in? (e.g., medical imaging, natural language processing, climate science, robotics, social media analysis) - Any particular data constraints you'd like to explore? (e.g., rare events, expensive annotation, limited labeled data, domain shift between training and deployment) - Any data-efficient ML techniques you're already curious about? (e.g., transfer learning, active learning, few-shot learning, self-supervised learning) - Do you have existing datasets or domain expertise you'd like to leverage? Please answer in a paragraph or two, being as specific as possible. If you already have a research question in mind, feel free to share it—but it's totally fine if you're still exploring! --- ## Question 2: Domain Expertise **Question type:** Long answer (paragraph) **Question text:** What domain(s) do you have background knowledge or expertise in? **Description/Helper text:** This could be from coursework, research, work experience, or personal interest. Examples: linguistics/NLP, computer vision, biology/medicine, climate science, social sciences, finance, education, robotics, etc. Understanding your domain expertise helps me: - Connect you with relevant datasets and applications - Suggest projects where you can meaningfully evaluate results - Form groups with complementary expertise If you don't have strong domain expertise yet, that's okay—just mention areas you're interested in learning about. --- ## Question 3: Data Access and Availability **Question type:** Multiple choice **Question text:** Do you already have access to data you'd like to use for your project? **Options:** - Yes, I have a specific dataset in mind and can access it - I have data access through my research/work but need to check permissions - No, but I know what kind of data I want to find - No, I'm open to using any publicly available dataset - Not sure yet **Description/Helper text:** Data access is often the biggest blocker for projects. Knowing your situation early helps me guide you toward feasible options. If you selected "Yes" or have data access through work/research, please mention the dataset briefly in Question 1 (Project Ideas) above. --- ## Question 4: Learning Goals **Question type:** Checkboxes (select all that apply) **Question text:** What specific techniques or skills from this course are you most excited to learn? **Options:** - Transfer learning and pre-trained models - Self-supervised and unsupervised learning - Semi-supervised learning - Active learning (strategic data selection) - Few-shot learning and meta-learning - Data augmentation strategies - Domain adaptation - Weak supervision - Working with computing clusters and GPU resources - Reading and implementing research papers - Other (please specify in Additional Information below) **Description/Helper text:** Select all that interest you. This helps me understand what you'd like to get out of the course and can help match you with appropriate project techniques. --- ## Question 5: Group Preferences **Question type:** Short answer **Question text:** Are there any classmates you'd prefer to work with on your project? **Description/Helper text:** Projects can be done individually or in groups of 2-3 students. If you'd like to work with specific classmates, list their names here. I'll use responses to suggest potential groupings based on shared interests, but group formation is ultimately up to you. If you prefer to work solo or don't have preferences yet, just write "No preference" or "Solo." --- ## Question 6: Python Programming **Question type:** Linear scale (1-5) **Question text:** Rate your comfort level with Python programming **Scale:** - 1 = Beginner (limited experience) - 2 = Some experience (can write basic scripts) - 3 = Comfortable (can implement ML pipelines with libraries like scikit-learn) - 4 = Proficient (comfortable with NumPy/pandas, can read and modify research code) - 5 = Expert (regularly write research code, familiar with advanced libraries) --- ## Question 7: Machine Learning **Question type:** Linear scale (1-5) **Question text:** Rate your comfort level with machine learning (theory and practice) **Scale:** - 1 = Beginner (this is my first ML course) - 2 = Some familiarity (know basic concepts like train/test splits, overfitting) - 3 = Comfortable (can train and evaluate standard models) - 4 = Proficient (understand theory, can implement and debug ML pipelines) - 5 = Expert (have done ML research or significant projects) --- ## Question 8: Command-Line and Computing Clusters **Question type:** Linear scale (1-5) **Question text:** Rate your comfort level with command-line interfaces and computing clusters **Scale:** - 1 = Beginner (rarely or never used command line) - 2 = Some familiarity (can navigate directories, run basic commands) - 3 = Comfortable (can use SSH, run scripts remotely) - 4 = Proficient (comfortable with job schedulers, environment management) - 5 = Expert (regularly use HPC resources for research) **Description/Helper text:** Don't worry if you're not comfortable with this yet—we'll have a hands-on session to get everyone set up on the computing cluster in class on Jan 29. --- ## Question 9: Additional Information (Optional) **Question type:** Long answer (paragraph) **Question text:** Is there anything else I should know that would help me support your learning this semester? **Description/Helper text:** This could include: - Specific learning goals for the course - Constraints on your time or availability - Preferred communication style - Accessibility needs - Anything else you think would be helpful