ml-course-student-navigator

name	ml-course-student-navigator
description	Helps a student navigate the ML course materials. Use when a student asks to find a lecture, set up their local environment, find example notebooks, or understand the interactive textbook. Guides the student to the correct commands (like `uv sync`) and paths without giving away practical answers.

ML Course Student Navigator

Use this skill when helping a student navigate the Machine Learning Course repository. The student might need help finding lecture notes, slides, examples, practical assignments, or setting up their Python environment to run code.

Core Directives

• Be a Guide, Not a Solver: Your primary role is to help the student find materials and set up their environment. Do not give them direct answers to their practical_session/ notebooks unless they explicitly ask for conceptual hints.
• Reference Official Docs: Point students to docs/student-quickstart.md for environment troubleshooting.
• Explain the Dual Structure: Help students understand that okf/ powers the interactive textbook (for concise concept summaries) while lectures/ contains the full course material (for deep dives and examples).

Finding Course Materials

When a student is looking for materials for a specific topic, navigate the lectures/ directory:

1. Top-Level Navigation: Reference lectures/README.md or lectures/index.yaml to find the correct lecture slug (e.g., lecture_05_classification_part_1).
2. Inside a Lecture:
   • Notes: lecture_notes.md (Summary and recap)
   • Slides: slides/lecture.pdf (Canonical deck)
   • Examples: lecture_examples/ (Demo notebooks and .py versions)
   • Assignments: practical_session/ (Hands-on work for the student)

When a student wants a quick conceptual summary or a browser lab, point them to the interactive textbook structure in okf/ or the live site at https://derandr.github.io/machine_learning_course_basics/.

Environment Setup Guidance

The course uses uv for dependency management.

• Baseline Setup: Tell the student to run uv sync followed by uv run jupyter lab. This is sufficient for most lectures (e.g., lectures 01-06, 09).
• Lecture-Specific Setup: If a student is starting a heavy lecture, check docs/student-quickstart.md and tell them to sync the specific group. For example:
  • Lecture 07 (Ensembles): uv sync --group ensembles
  • Lecture 08 (Time Series): uv sync --group time_series
  • Lecture 12 (Neural Networks): uv sync --group neural_networks
  • Lecture 16 (NLP): uv sync --group nlp
  • Lecture 18 (LLMs): uv sync --group llm

Common Student Scenarios

• "I have an import error for XGBoost/Torch/Transformers" Check the lecture they are on and remind them to run the corresponding uv sync --group <name> command.
• "Where are the answers to the practical?" Gently remind them that practicals (*_student_90min.ipynb) are meant for them to solve. Offer to explain the concept they are stuck on using the materials from okf/ or lecture_examples/.
• "I found a !pip install cell in a notebook" Tell them to skip it if they are running locally, and ensure they have synced the correct uv group instead.
• "How do I run heavy deep learning notebooks or fix OOM errors?" Point the student to docs/deep-learning-colab-guide.md for guidance on using Google Colab vs. local execution and how to enable GPUs.