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EVENT DETAILS |
DETAILS
Each class is 20 hours of classroom guidance with an optional three week-long showcase project of students' own choices and optional presentation of their projects.
Date: Sundays |
May 17th, 31th, Jun 7th, 14th, 28th (We take breaks on Memorial Weekend on May 24rd and Father's Day on Jun 20th)
Time:
1:00 p.m. to 5:00 p.m.
Instructor:
Vivian Zhang, CTO of SupStat., Founder of NYC Data Science Academy, Adjunct Professor at NYU and Stony Brook Univ., with Masters Degree of Computer Science, and Masters Degree of Applied Math and Statistics
Venue:
205 E 42nd Street, 16th Floor, New York, NY 10017 (5 min from Grand Central)
Online course option:
Students can take it remotely through recorded youtube sessions with google hangout TA support, email info@nycdatascience.com to get enrolled
For corporate training or small group training inquiry:
Email info@nycdatascience.com to get corporate/group discount
Project Demo Day and Certificates
From simple linear regression to support vector machines and clustering algorithms, this course ends with Project Demo Day. On Demo Day you will access and analyze real data, utilizing the tools and skill sets taught to you throughout the course. Upon successful completion of the course, you will qualify for one of three certificates: Extraordinary Standing, Honorable Graduation, and Active Participation.
Certificates are awarded according to your understanding, skill, and participation. Basic Python programming background is prerequisite needed for the course.
FAQ
1. Do I have to do three weeks project? Is it required for taking this class?
Students could choose to spend extra 3 weeks with the teaching crew to do a project of their own choices. We are happy to offer assistance and arrange presentation to demo their work.
2. Can I take class online if I am not in NYC?
You can take it onsite or through recorded sessions on Youtube and get timely assistance from teaching crew by google hangout or Skype.
3. If I have to miss some session, how can I make it up?
We record all of our classes and make it available for students right after each class. If you miss a class, you can also get extra help such as office hour or internet support through google hangout or Skype.
SYLLABUS
Week 1 - Introduction
What is Machine Learning
Mathematics review
Linear Regression
Multivariate linear regression
Lab: Numpy/Scikit-Learn
Week 2 - Regression and Classification
Naive Bayes Classifiers
k-Nearest Neighbors
Logistic Regression
Linear Discriminant Analysis
Lab: Supervised Learning
Week 3 - Resampling and Model selection
Cross-validation
Bootstrap
Feature selection
Lab: Model selection and regularization
Week 4 - Support Vector Machines and Decision Trees
Support Vector Machines
Decision Trees
Forests
Lab: Decision Trees and SVMs
Week 5 - Unsupervised Learning
Principal Component Analysis
Clustering with K-Means
State Estimation
Lab: PCA and clustering
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