11-755 · Fall 2026
Schedule
Fall 2026 lecture calendar
Lecture dates, quizzes, homeworks and project checkpoints for Fall 2026. Regular meetings are Tuesday and Thursday, 2:00pm–3:20pm, in GHC 4307. The order of topics can still shift with class discussion.
When
Tuesday and Thursday
2:00pm–3:20pm
Where
GHC 4307
Gates Hillman Center
Holiday / break
No class
| Date | Lecture | Topic | Quiz | Homework & project |
|---|---|---|---|---|
| Tue, Aug 25 | 1 | Introduction | — | — |
| Thu, Aug 27 | 2 | Linear Algebra 1 | Quiz 1 | — |
| Tue, Sep 01 | 3 | Linear Algebra 2 | — |
|
| Thu, Sep 03 | 4 | Optimization | Quiz 2 | — |
| Tue, Sep 08 | 5 | Deterministic Representations | — | — |
| Thu, Sep 10 | 6 | Data-Driven Representations | Quiz 3 | — |
| Tue, Sep 15 | 7 | Non-Negative Matrix Factorization | — | — |
| Thu, Sep 17 | 8 | Adaboost and Face Detection | Quiz 4 |
|
| Tue, Sep 22 | 9 | Probability and Information Theory | — |
|
| Thu, Sep 24 | 10 | Independent Component Analysis | Quiz 5 | — |
| Tue, Sep 29 | 11 | Clustering | — | — |
| Thu, Oct 01 | 12 | Sparse and Overcomplete Representations | Dictionary Representations | Quiz 6 | — |
| Tue, Oct 06 | 13 | Regression and Prediction | — |
|
| Thu, Oct 08 | 14 | Linear Classifier | Quiz 7 |
|
| Tue, Oct 13 | — | No Class - Fall Break | — |
|
| Thu, Oct 15 | — | No Class | — | — |
| Tue, Oct 20 | 15 | Canonical Correlation Analysis | — | — |
| Thu, Oct 22 | 16 | ARMA | — | — |
| Tue, Oct 27 | 17 | Expectation Maximization 1 | Quiz 8 |
|
| Thu, Oct 29 | 18 | Expectation Maximization 2 | — | — |
| Tue, Nov 03 | — | No Class - Democracy Day | — | — |
| Thu, Nov 05 | 19 | Factor Analysis | Quiz 9 | — |
| Tue, Nov 10 | 20 | Hidden Markov Models | Quiz 10 |
|
| Thu, Nov 12 | 21 | HMM2 | — | — |
| Tue, Nov 17 | 22 | Kalman Filtering 1 | Quiz 11 | — |
| Thu, Nov 19 | 23 | Kalman Filtering 2 | — | — |
| Tue, Nov 24 | 24 | Particle Filtering | — | — |
| Thu, Nov 26 | — | Thanksgiving break | — | — |
| Tue, Dec 01 | 25 | Maxent models and Survival Analysis | Quiz 12 | — |
| Thu, Dec 03 | 26 | Time to Predict Very Rare Events | — |
|
Quiz numbers follow the source calendar (quizzes 1–12; there is no Quiz 13 or 14). HW3 Release is Oct 13; HW3 Due/HW4 Release is Nov 10. HW4 due date is not listed. Exam week is not listed.
Coursework dates
Weights are on the syllabus. Handouts stay on Canvas, not this page.
- Homeworks Four assignments HW1: Sep 1–17. HW2: Sep 22–Oct 8. HW3: Oct 13–Nov 10. HW4 Release Nov 10 (due date not listed).
- Quizzes Weekly (see table) Short multiple-choice quizzes on the previous week's material. Numbered 1–12 on the source calendar.
- Group project Document, teams, proposal, checkpoint, report Document out Sep 17. Team form Oct 6. Proposal Due Oct 27. Mid-term check point Nov 10. Final report Dec 3. Presentation date TBD.
What the course covers
Grouped by theme — the table above is the dated order.
-
Module 1
Foundations
- Introduction to machine learning for signal processing
- Linear algebra for signals, part 1
- Linear algebra for signals, part 2
- Optimization
- Probability and information theory
-
Module 2
Representations
- Deterministic representations
- Data-driven representations
- Non-negative matrix factorization
- Sparse and overcomplete representations
- Dictionary learning
-
Module 3
Learning from signals
- Boosting and face detection
- Clustering
- Regression and prediction
- Linear classifiers
- ARMA models
-
Module 4
Latent variable models
- Independent component analysis
- Canonical correlation analysis
- Expectation maximization, part 1
- Expectation maximization, part 2
- Factor analysis
- Maximum entropy modeling
-
Module 5
Sequences and state-space models
- Hidden Markov models, part 1
- Hidden Markov models, part 2
- Kalman filtering and tracking
- Extended and non-linear filtering
-
Module 6
Applications and special topics
- Survival analysis
- Predicting rare events
- Course project presentations