2 Schedule
The schedule is subject to change. Here’s the most updated version:
| Week | Date | Lecture | Notes |
|---|---|---|---|
| 1 | Aug 25 (Tue) | Introduction | |
| First Third | SENSORS | ||
| 1 | Aug 27 (Thu) | Physical Principles of Sensing | |
| 2 | Sep 1 (Tue) | Electrical Circuits | |
| 2 | Sep 3 (Thu) | Fundamentals of Data Acquisition | |
| 3 | Sep 8 (Tue) | Signal Conditioning and Sampling | |
| 3 | Sep 10 (Thu) | Frequency Domain Analysis | |
| 4 | Sep 15 (Tue) | Measurement Error and Uncertainty | |
| Second Third | DATA | ||
| 4 | Sep 17 (Thu) | Time-Series Data: Representation and Processing | |
| 5 | Sep 22 (Tue) | Relational Databases and the Entity-Relationship Model | |
| 5 | Sep 24 (Thu) | The Structured Query Language (SQL) | |
| 6 | Sep 29 (Tue) | Database Design Principles | |
| 6 | Oct 1 (Thu) | Data Representation and Compression | |
| 7 | Oct 6 (Tue) | Other Database Models: Graphs, Objects, Key-Value Stores | |
| 7 | Oct 8 (Thu) | Metadata and the Resource Description Framework | |
| 8 | Oct 13 (Tue) | FALL BREAK | No Class |
| 8 | Oct 15 (Thu) | FALL BREAK | No Class |
| 9 | Oct 20 (Tue) | MID-TERM EXAM | Covers Sensors and Data |
| Last Third | MODELS | ||
| 9 | Oct 22 (Thu) | Principles of Statistical Learning | |
| 10 | Oct 27 (Tue) | Linear Models for Regression | |
| 10 | Oct 29 (Thu) | Linear Models for Classification | |
| 11 | Nov 3 (Tue) | DEMOCRACY DAY | No Class |
| 11 | Nov 5 (Thu) | Beyond Linearity | |
| 12 | Nov 10 (Tue) | Tree-Based Methods | |
| 12 | Nov 12 (Thu) | Unsupervised Learning | |
| 13 | Nov 17 (Tue) | Neural Networks and Sequential Models | |
| Coda | SENSORS, DATA, AND MODELS | ||
| 13 | Nov 19 (Thu) | Putting It All Together | Sensors, Data and Models |
| 14 | Nov 24 (Tue) | Project Feedback / Guidance Session (Zoom) | |
| 14 | Nov 26 (Thu) | THANKSGIVING | No Class |
| 15 | Dec 1 (Tue) | Project Feedback / Guidance Session | |
| 15 | Dec 3 (Thu) | Project Feedback / Guidance Session |
Additional Dates:
- Friday, December 4: In-person final project demo/poster session (last day of classes). Digital copy of the poster, code, and datasets due by end of day.