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CS82: Statistical Learning

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Prerequisites: Completion of CS01b or AP CS, or permission of instructor. Also requires Algebra II math experience.

CS82 is a math heavy course offered at KTBYTE, and require students to have mastered self-guided learning. Students will learn tools to model and understand complex data sets, tools and algorithms that are commonly used for tackling "Big Data" problems. Covered topics include different techniques in supervised learning, unsupervised learning and reinforcement learning. This course is taught in Python using the numpy and sk-Learn libraries. Students will have roughtly 2 hours of homework assignments per week, plus a final project due at the end of the semester.

Course Content

  • Python Basics, Numpy and Data Formats
  • Descriptive Statistics
  • Basic Linear Algebra
  • Plots
  • Linear Regression
  • Logistic Regression
  • Naive Bayes Classifier
  • Q-Learning
  • Neural Networks
  • Principal Component Analysis
Format: Web-conference

We offer this course in the following durations

CS82: Statistical Learning

Meeting Location

Online Live Webconference

We will send you the webconference link once enrolled.

CS82 meets one hour a week over the semester. There are weekly homework assignments as well as a final project.

Wed Jan 31 - Wed Jun 13

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  • 8:40-9:40pm ET
  • Advanced office hours for questions
  • Includes weekly homework & review content
  • Includes VM

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