Physics and physical sciences: ML domain track
You already think in models, measurement error, and experiments. This track shows which parts of the shared Road to ML spine matter first for physical-science work. It does not replace a physics degree. It does not guarantee a research post or industry hire.
Core idea. Keep your domain depth. Add a thin, honest ML methods path. Use Modules 00–25 for methods. Use this syllabus for order and emphasis.
Who this is for
- Physics / astronomy / applied physics students who need ML for thesis or lab data
- Researchers who want careful supervised learning and uncertainty habits
- Switchers from physical sciences who want a Data Scientist emphasis without skipping evaluation
What you already bring
- Comfort with equations, units, and approximate models
- Lab or simulation data with noise and systematic error
- Respect for measurement limits (that maps cleanly onto leakage and generalization)
ML problem types you will meet
- Regression on continuous observables
- Time series and signals (detectors, sensors, orbits, lab traces)
- Anomaly and quality flags on instrument streams
- Surrogate models that approximate expensive simulations (only after you can evaluate them)
- Classification when labels are discrete states of a system
Gaps this track closes on the shared spine
- Python and tabular workflows (Module 01)
- Supervised learning and evaluation discipline (Modules 02–05)
- Time series habits (Module 15)
- Explaining model behavior when a collaborator asks why (Module 21)
Related resources
- Math Formulas. Quick math sheet beside Module 00 work.
- Prerequisites Cheatsheet. Baseline math and Python habits in one place.
- Data Validation. Measurement noise and instrument quirks need checks.
- Model Explainability Cheatsheet. Quick sheet beside Module 21.
Role emphasis (not destiny)
| Emphasis | Hub / README role | Why |
|---|---|---|
| Primary | Data Scientist | Predictive models, evaluation, and careful experiments match lab culture |
| Alternate (later) | ML Engineer | When you must ship a model into a pipeline, not only analyze offline |
See the main Career Paths table and Career Roadmap Guide. Treat times as emphasis maps only.
Intensity maps
Pick one. You can change later.
Research support (tier B)
Goal: trustworthy analysis for papers, theses, and lab decisions.
- Module 00 if math or Python is shaky
- Module 01
- Module 02
- Module 03
- Module 05 (do not skip)
- Module 15 if your data is ordered in time
- Module 21
- One beginner project from Module 16 that practices regression or forecasting habits
Optional later: Module 08 for structure discovery. Module 09–10 only when simpler models fail for a clear reason.
Job-oriented study (tier B toward C)
Goal: portfolio evidence that you can clean data, train, evaluate, and explain. Still no hire guarantee.
- Complete the Research support list
- Add Module 04 and Module 07
- Add Module 19 if your workplace data lives in tables
- Add Module 13–14 only when you need production habits
- Prefer one intermediate project from Module 17 that you can defend end to end
Ordered study checklist
Use this as your weekly spine.
- Skim this syllabus and pick Research support or Job-oriented study
- Finish Module 01 until you can load, clean, and plot a noisy CSV on your own
- Finish Module 02 until you can state train vs serve vs eval in your own words
- Finish Module 03 with a physics-flavored dataset of your own (even a small public one)
- Finish Module 05 and write down your split rule (time, run id, or experiment batch)
- If signals matter, finish Module 15 before deep models
- Read Module 21 and practice explaining one wrong prediction
- Ship one small project README with data source, split, metric, and failure case
Start here
Open Module 00 README if you need math or environment help. Otherwise open Module 01 README.
Honesty and traps
- Fancy deep models without Module 05 habits will impress nobody who reads papers carefully.
- Do not call a notebook “production ML.”
- Medical imaging that sits near physics stays educational. It is not clinical advice.
- Surrogate modeling of simulations needs error bars and domain checks. A low loss is not physical truth.
Where this connects
Domain chapters for physics (literacy, uncertainty, methods map) will land in this folder next. Until then, the Module NN links above are the teaching content.
Try next: Open Module 01: Python for Data Science and complete its core path with a noisy measurement CSV of your choosing.