Mondays from 2:00pm-3:40pm SILV_520 (Silver Center for Arts & Science, 100 Washington Square East, room 520)
DS-GA 1018.002 Lab (cap = 40) Wednesday from 3:45pm-4:35pm SILV_520 (Silver Center for Arts & Science, 100 Washington Square East, room 520)
DS-GA 1018.003 Lab (cap = 40) Wednesday from 9am-9:50am GCASL_475
Cristina Savin, [email protected] Office hours: TBD
Section 002 - Haresh Rengaraj Rajamohan ([email protected])
Section 003 - Ying Wang ([email protected])
Office hours: TBD
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. Brightspace version is always up-to-date and to be used as main reference.
problem sets (25%) + midterm exam (20%) + final project (25%) + lab(20%) + participation(10%)
Participation: piazza, engagement during lectures, labs, and office hours
We will use Piazza as the main platform for communication, for 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 Oct 22th.
*Check with CS if you are considering working individually or in a larger group.
Lecture videos will be posted to NYU Classes and we will be providing zoom access when students are unable to come to class (due to quarantine, issues with travel, etc). Class attendance is generally required.
| Date | Lecture | Assignments |
|---|---|---|
| Sept. 13 | Lecture 1: Logistics. Introduction to time series. Graphical models | |
| Sept. 15 | [Recitation] | |
| Sept. 20 | Lecture 2: Basic statistics of time series. AR: inference and learning | |
| Sept. 22 | Lab 1: AR | |
| Sept. 27 | Lecture 3: ARIMA models | |
| Sept. 29 | Lab 2: ARIMA | |
| Oct. 4 | Lecture 4: LDS, Kalman filtering | |
| Oct. 6 | Lab 3: Inference in LDS | |
| Oct. 12 | Lecture 5: EM Kalman | |
| Oct. 13 | Lab 4: LDS parameter learning | |
| Oct. 18 | Lecture 6: Particle filtering | Project proposal due |
| Oct. 20 | Lab 5: Particle filtering | |
| Oct.25 | Lecture 7: Hidden Markov Models | |
| Oct.27 | Lab 6: HMMs | |
| Nov.1 | Lecture 8: Links between models, generalizations. | |
| Nov.3 | Midterm recap (no lab) | |
| Nov.8 | Mid-term exam | |
| Nov.10 | No lab. Projects Q&A | |
| Nov.15 | Lecture 8: Intro to GPs | |
| Nov.17 | Lab 7: GP regression | |
| Nov. 22 | Lecture 10. Deep learning for time series | |
| Nov. 24 | Lab 8: RNNs | |
| Nov.29 | Lecture 11. Spectral methods | |
| Dec.1 | Lab 9: Spectral methods | |
| Dec.6 | 12. Guest lecture: Text generation, transformers (He He) | |
| Dec. 8 | No lab | |
| Dec. 13 | Final projects presentation | Project reports due Dec.20th |
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.
- Credit should be explicitly given for any code you use that you did not write yourself.
- Violations result in a zero score on that assignment, and a notice to the DGS.
Penalties: 20% points off assignment for each extra day of delay.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent