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Description

Official Description

8.0 Continuing Education Units (CEUs)

Fundamental statistical machine learning concepts and tools using Python. Emphasis on descriptive statistics, statistical distributions, random number generation, basic data visualization; linear regression; basic classification; error estimation: cross-validation, bias-variance trade-off; shrinkage methods; dimension reduction; beyond linearity: smoothing splines, local regression, additive models; tree and ensemble methods; powerful classifiers; unsupervised learning.

 

Supplementary Information

20 hours of lectures and 60 hours of independent study, assignments and readings; course includes synchronous and asynchronous activities.

 

(30 INFORMS PDUs)

The PDUs earned through this non-credit-bearing course can be used by CAP and aCAP certificants to satisfy the 30 PDUs needed to renew their certification every three (3) years. More Info.

Topics Covered

  • Introduction to Regression and Classification Analysis
  • Model Evaluation and Resampling Methods
  • Model and Variable Selection
  • Dimension Reduction
  • Tree-Based Methods and Ensemble Learning
  • Support Vector Machines
  • Introduction to Unsupervised Learning

Learning Outcomes

The course is designed to enable you to:

  • Use python to visualize and execute a predictive model;
  • Install, load, and use conventional libraries related to machine learning and statistical modeling;
  • Combine different algorithms in order to execute a predictive task;
  • Implement parameter estimation and regularization;
  • Perform model selection and model inference;
  • Improve predictive performance by combining various algorithms;
  • Interpret results of applied statistical methods.

Notes

This course is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalized feedback on every exercise.

Prerequisite(s) and Corequisite(s)

Applies Towards the Following Programs

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Section(s) offered
Section Title
Statistical Machine Learning
Language of Delivery
English
Type
Online Course, fixed date
Days
T
Time
6:00PM to 9:00PM
Dates
Sep 03, 2024 to Nov 05, 2024
Schedule and Location
Contact Hours
30.0
Delivery Format(s)
Course Fee(s)
Fee non-credit $1,377.64 Click here to get more information
Drop Request Deadline
May 06, 2024 to Sep 10, 2024
Transfer Request Deadline
May 06, 2024 to Sep 10, 2024
Withdrawal Request Deadline
Sep 10, 2024 to Sep 17, 2024
Section Notes

A minimum number of registrations is required for this course section to be offered. The School reserves the right to cancel any course section when a minimum number of registrations has not been reached 7 days prior to the start date. In the event of a cancellation, the course fee will be refunded in full.

Course Format

This online course is a combination of weekly live online instructor-led sessions from 6:00 - 7:30 PM and self-directed learning activities and assignments.

Course Drop/Withdrawal Policy

  • Any time prior to the 1st class: Course Drop Period with Full Refund.
  • After the 1st and before the 2nd class: Course Withdrawal Period with Full Refund.
  • After the 2nd class before the 3rd class: Course Withdrawal with No Refund.

 

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