| Week | Monday | Wednesday |
|---|---|---|
| 1 | 1/26/15 Snow Day |
1/28/15 Introductions, Course Review |
| 2 | 2/2/15 Handling Data with Pandas |
2/4/15 Visualizing Data in Python |
| 3 | 2/9/15 Statsmodels and the Linear Regression |
2/11/15 Time Series Analysis and Autoregression Techniques |
| 4 | 2/16/15 No Class - President's Day |
2/18/15 Machine Learning Workflow and SKLearn |
| 5 | 2/23/15 Project 1 Presentations |
2/25/15 Logistic Regression |
| 6 | 3/2/15 NLTK and Text Analysis |
3/4/15 Classifying Text with Naive Bayes |
| 7 | 3/9/15 Project 2 Presentations |
3/11/15 Machine Learning Review and where we go from here |
| 8 | 3/16/15 Decision Trees and Ensembles |
3/18/15 Support Vector Machines and Kernels |
| 9 | 3/23/15 Clustering and K Means |
3/25/15 Dimension Reduction |
| 10 | 3/30/15 Recommendations |
4/1/15 Web Servers and Data Products |
| 11 | 4/6/15 Database Technologies |
4/8/15 Distributed Systems and MapReduce |
| 12 | 4/13/15 Workshop |
4/15/15 Workshop |
| 13 | 4/20/15 Final Presentations |
Instructions for each assignment is listed in the assignments folder. Each assignment will explain details, goals, where to find the data if necessary, and a benchmark rubric for personal alignment.
All assignments are introduced on Wednesdays and due the coming Monday. Instructors will provide feedback before the coming Wednesday class.
There are no assignments when there are project presentations coming up.
All weekly assignments are graded on completion only. We understand that students can't always get to everything, or can't always put in 100% effort due to life, work, etc. We will, however, provide feedback to all submitted work, and a placement on the rubric, if there was one. You are always more than welcome to resubmit work, though our priority for feedback will always be what is upcoming.
- Linkable assignments, such as gists, can be submitted by posting to this submission form. We highly recommend using git to checkin your work, as it's a great starting point to a portfolio, but other formats are fine (a blog post you wrote up, a gist, file/archive through google drive or dropbox)
Instructions for each project is listed in the projects folder. Each project will explain details, goals, and a grading rubric.
Consider the three projects as one long project, as the projects should build on top of each other, and show an evolution of work. You are primarily being graded on this evolution, so keep that in mind!
While assignments are graded on a check system, projects are graded via a rubric. We'll review the rubrics as projects are introduced.
Please use the same google form for assignments.
| instructor | day/time available | by |
|---|---|---|
| Ed | Tuesday, all day | in person, hangouts, slack |
| Friday, all day | in person, hangouts, slack
Pooja | Tuesday, 6 - 7 PM | in person, hangouts, slack
| Saturday, all day | in person (10:30 AM - 12:30 PM @ GA West 3rd floor), hangouts, slack
Julia | Thursday, Saturday
Hours (TBD) | in person, hangouts, slack
Please use email to reach out about grabbing office hours. Use [office hours] in the subject line as it can help us find the emails easier and reply more quickly.
You've all been invited to use Slack for chat during class and the day. Please consider this the primary way to contact other students. The TAs will be in Slack during class to handle questions. All instructors will be available on Slack during office hours (listed above).
If you're not already "watching" the github repository, please do so.
You can check the watcher's list to see if you're on it.