Mon, 2:00-3:40pm, in 60 5th Av, Room 110
Wed, 3:30- 4.20pm in 60 5th Av, Room 110
Cristina Savin, csavin@nyu.edu
Office hours: Mo, 4:00-5:00pm, Room 608
Caroline Haimerl, ch2880@nyu.edu and Yiqiu (Artie) Shen, ys1001@nyu.edu
Office hours: Thu, 11am-12pm
This graduate level course presents fundamental tools for characterizing data with statistical dependencies over time, and using this knowledge for predicting future outcomes. These methods have broad applications from econometrics to neuroscience.The course emphasizes generative models for time series, and inference and learning in such models. We will cover range of approaches including Kalman Filter, HMMs, AR(I)MA, Gaussian Processes, and their application to several kinds of data.
Note: information presented is tentative, syllabus may be subject to change as course progresses.
problem sets (25%) + midterm exam (25%) + final project (25%) + lab(20%)+participation(5%)
Participation: piazza, engagement during lectures, labs, and office hours
We will usePiazzafor announcements, and discussions about the course. Interactions on Piazza, particularly good answers to other students' questions, will count toward the participation grade.
Work in groups of 2-3 students.* Topics are flexible, including applying know algorithms to an interesting dataset, reviewing and implementing a state of the art solution, to improving an existing algorithm. Project proposals due in week 4. *Check with CS if you are considering working individually or in a larger group.
Lecture videos will be posted to NYU Classes. Class attendance is still required.
| Date | Lecture | Assignments |
|---|---|---|
| Sept. 4 | [no lab] | |
| Sept. 9 | Lecture 1: Logistics. Introduction. Basic statistics for characterizing time series. | |
| Sept. 11 | [Recap basic Bayes, graphical models] | |
| Sept. 16 | [Lecture 2: AR(I)MA] | |
| Sept. 18 | [Lab 1: ARIMA] | |
| Sept. 23 | [Lecture 3: LDS, Kalman filtering] | |
| Sept. 25 | [Lab 2: Inference in LDS] | |
| Sept. 30 | [Lecture 4: Particle filtering] | |
| Oct. 2 | [Lab 3: LSD parameter learning] | |
| Oct. 7 | [Lecture 5: Hidden Markov Models | Project proposal due |
| Oct. 9 | [Lab 4: Particle filtering] | |
| Oct.15 TUE! | Lecture 6: a unified view of linear models | |
| Oct.16 | Lab 5: HMMs | |
| Oct.21 | Introduction to Gaussian Processes | |
| Oct.23 | No lab. Office hours | |
| Oct.28 | Mid-term exam | |
| Oct.30 | No lab | |
| Nov. 4 | GP advanced topics.Intro to RNNs | |
| Nov. 6 | Lab GP | |
| Nov.11 | Deep learning for time series | |
| Nov.13 | RNNS lab | |
| Nov.18 | Spectral methods 1 | |
| Nov. 20 | Lab spectral methods | |
| Nov. 25 | Spectral methods 2 | |
| Nov. 27 | Thanksgiving | |
| Dec. 2 | Guest lecture: Joan Bello | |
| Dec. 4 | No lab. Work on projects | |
| Dec. 9 | Final projects presentation | Project reports due Dec.15 |
| Dec. 11 | No lab |
There is no required textbook. Assigned readings will come from freely-available online material.
- Time series analysis and its applications, by Shumway and Stoffer, 4th edition
- Pattern recognition and machine learning, Bishop
- Gaussian processes Rassmussen & Williams
- Review notes from Stanford's machine learning class
- Sam Roweis's probability review
- Carlos Ferndandez's notes on Statistics and Probability for Data Science DS-GA 1002
We expect you to try solving each problem set on your own. However, if stuck you should discuss things with other students in the class, subject to the following rules:
- Brainstorming and verbally discussing the problem with other colleagues ok, going together through possible solutions, but should not involve one student telling another a complete solution.
- Once you solve the homework, you must write up your solutions on your own.
- You must write down the names of any person with whom you discussed it. This will not affect your grade.
- Do not consult other people's solutions from similar courses.
- Violations result in a zero score on that assignment, and a notice to the DGS.
Try to solve problems on your own first. If you get stuck, you can discuss homework questions with colleagues, but you need to write up the final solution individually. Credit should be explicitly given for any code you use that you did not write yourselves.Late submission penalties: 20% points off for each extra day of delay.