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
Remote Zoom cmu.zoom.us/j/97661004908
Holiday / break No class
Fall 2026 lecture calendar
Date Lecture Topic Quiz Homework & project
Tue, Aug 25 1 Introduction
Slides Recording
— —
Thu, Aug 27 2 Linear Algebra 1
Slides
Quiz 1 —
Tue, Sep 01 3 Linear Algebra 2
Slides
—
  • HW1 Release
Thu, Sep 03 4 Optimization
Slides
Quiz 2 —
Tue, Sep 08 5 Deterministic Representations
Slides
— —
Thu, Sep 10 6 Data-Driven Representations
Slides
Quiz 3 —
Tue, Sep 15 7 Non-Negative Matrix Factorization — —
Thu, Sep 17 8 Adaboost and Face Detection Quiz 4
  • HW1 Due
  • Document out
Tue, Sep 22 9 Probability and Information Theory —
  • HW2 Release
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 —
  • Team form
Thu, Oct 08 14 Linear Classifier Quiz 7
  • HW2 Due
Tue, Oct 13 — No Class - Fall Break —
  • HW3 Release
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
  • Proposal Due
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
  • HW3 Due/HW4 Release
  • Mid-term check point
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 —
  • Final report

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