Data Science for Mechanical Systems
Columbia University · MECE 4520 · Fall 2026 · Vergil
MECE 4520 introduces data science and machine learning through real engineering systems.
- Location: 717 Hamilton Hall
- Time: Tuesday & Thursday 10:10am - 11:25am
- Lecturer: Changyao Chen (cc2759@columbia.edu)
- TAs: Mai Al Shaaban (ma5070) and Vlad Pyltsov (vp2517)
- Office hours: TBD
Course materials and source files are available on GitHub.
Course focus
Rather than treating machine-learning methods as disconnected algorithms, the course follows a practical engineering progression:
understand engineering data → build predictive models → diagnose systems → learn representations → make decisions and control systems
Students will work repeatedly with a small number of real mechanical-engineering datasets, including gas-turbine emissions and CNC milling tool-wear data. This makes it possible to connect model choices to the systems, sensors, variables, and operating conditions that produced the data.
Grading policy
The course grade is based on:
| Component | Weight |
|---|---|
| Homework assignments | 30% |
| In-class quizzes | 30% |
| Final project | 40% |
Students should expect approximately five homework assignments. The final project will be completed in groups and presented as a five-minute presentation on a selected engineering-data topic.
In-class quizzes
Expect a short, time-limited quiz about once each week. At the end of the semester, your 3 lowest quiz scores will be dropped.
We will use Poll Everywhere for in-class quizzes. You need to sign in Poll Everywhere with your Columbia UNI before the first quiz.
Syllabus
This syllabus may evolve as the Fall 2026 course is finalized.
| Class dates (2026) | Topic |
|---|---|
| Before Sep. 8 | Python, NumPy/Pandas, linear algebra, and probability refresher |
| Sep. 8 — asynchronous Sep. 10 |
Course launch, Python/data foundations, engineering EDA, and measurement variability |
| Sep. 15 Sep. 17 — asynchronous |
Simple and multiple linear regression |
| Sep. 22 & 24 | Statistical inference for regression, prediction intervals, and regularization |
| Sep. 29 & Oct. 1 | Logistic regression and classification metrics |
| Oct. 6 & 8 | Validation, leakage, grouped splits, and temporal train/test splits |
| Oct. 13 & 15 | Decision trees, random forests, and gradient boosting |
| Oct. 20 & 22 | PCA, dimensionality reduction, and clustering |
| Oct. 27 & 29 | Neural networks and nonlinear function approximation |
| Nov. 3 — Election Day (no class) Nov. 5 |
Sequential data, degradation, and optional signal processing |
| Nov. 10 & 12 | Reinforcement-learning foundations: bandits, states, actions, rewards, and MDPs |
| Nov. 17 & 19 | Bellman equations, dynamic programming, Q-learning, and SARSA |
| Nov. 24 Nov. 26 — Thanksgiving (no class) |
Function approximation and deep Q-networks |
| Dec. 1 & 3 | Policy gradients, PPO, continuous control, and RL versus classical control |
| Dec. 8 & 10 | Final project presentations |