Class Description
The workshop will focus on knowledge extraction and discovery from data, using statistical tools and machine learning algorithms. The students will be required to design and implement such systems and present their results in class.
Meeting Schedule
| # |
Date |
Class Details |
Lecturer |
Files |
| 1 |
27/10/2019 |
Introduction: Intro to data science, project details, important Dates |
Daniel Deutch |
Slides |
| 2 |
03/11/2019 |
Hands-On Data Science in Python : Jupyter Notebook, Numpy, Scipy, Scikit-Learn, Pandas |
Amit Somech |
Slides
Material (Notebook, data files)
|
| 3 |
22/12/2019 |
Student Presentations #1: 5 minutes, 5 slides - Presentation of initials results |
|
|
| 5 |
26/01/2020 |
Student Presentations #2: (Almost) Final presentations: Problem, model, techniques, results |
|
|
| 6 |
01/03/2020 |
Projects Submission Deadline |
|
|
Notifications
| Date |
Notification |
| 23/10/2019 |
First task: (1) Send us by email your team members (names & emails) NO LATER THAN 10/11/2019
(2) After you form a team, choose a dataset from here
|
| 31/10/2019 |
Second task: (1) Submit (email) a self-contained presentation (no longer than 15 slides) that describes your dataset of choice, some results of initial analysis (+visualizations), and project problem formulation. If exist, review what has been done in previous work (academic papers or data science notebooks) and how is your project different. (2) Last submission date is 01/12/19 . (3) After submitting, we will send back comments and potentially some followup questions. Some teams may be encouraged to attend an office hour (although all teams/individuals are welcome to schedule an office hour ). Important: Team must receive a written confirmation regarding the dataset and problem. Also, any change in an already approved dataset/problem needs to be approved again.
|
| 01/11/2019 |
Final project guidelines: here (PDF) |
Course Grading
- Class presentation: 10%
- Final Project: 90%
Resources