Statistics 311/Electrical Engineering 377: Information Theory and Statistics
Approximate Course Schedule
The syllabus below suggests what will (likely) be our approximate course
schedule. We will likely change a few things around as the course
continues, and we may even omit topics or add others as the class desires.
While we skip some chapters, we encourage students to at least skim
through them (for example, Chapter 3 on exponential families will
provide useful background, especially if students have not seen them
before). When reading is optional (but provides good context), we will
add an asterisk (*) to it.
| Lecture | Date | Topics | Reading |
| 1 | Tue, Sep 22 | Overview, basic divergence measures | LN 1-2, CT 2* |
| 2 | Thu, Sep 24 | Chain rules and general divergence measures | LN 2, CT 2 |
| 3 | Tue, Sep 30 | Le Cam and Fano inequalities, concentration | LN 2, 4.1, RM 2.3 |
| 4 | Thu, Oct 2 | Sub-exponential concentration | LN 4.1, 4.2, RM 2.4 |
| 5 | Tue, Oct 7 | Martingale methods and uniformity | LN 4.2, 4.3, RM 2 |
| 6 | Thu, Oct 9 | Uniform laws, beginning PAC-Bayes bounds | LN 5.1, 5.2 |
| 7 | Tue, Oct 14 | PAC-Bayes bounds and bits of interactive data analysis | LN 5.2 |
| 8 | Thu, Oct 16 | Interactive data analysis | LN 5.3 |
| 9 | Tue, Oct 21 | Privacy and disclusure limitation | LN 7.1, 7.2 |
| 10 | Thu, Oct 23 | Privacy: composition guarantees | LN 7.2, 7.3 |
| 11 | Tue, Oct 28 | Privacy: inverse sensitivity | LN 7.4 |
| 12 | Thu, Oct 30 | Le Cam/Fano methods | LN 8.1–8.4 |
| Tue, Nov 4 | No class (democracy day) | |
| 13 | Thu, Nov 6 | Assouad's method | LN 8.5–8.6 |
| 14 | Tue, Nov 11 | Strong data processing inequalities | LN 9.1–9.2 |
| 15 | Thu, Nov 13 | Constrained lower bounds | LN 9.1–9.2 |
| 16 | Tue, Nov 18 | Loss functions and entropy | LN 11.1–11.3 |
| 17 | Thu, Nov 20 | Calibration and proper losses | LN 12.1–12.3 |
| 18 | Tue, Dec 2 | Surrogate risk consistency | |
| 19 | Thu, Dec 4 | Presentations |
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Abbreviation key
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