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CS407

Principles of Machine Learning

Course DirectorySNHU CS407 has 2 direct prerequisites, with 15 courses in its complete prerequisite tree.

Undergraduate · 3 credits

With the exponential growth of both available data and computing power, Machine Learning becomes increasingly important and essential knowledge. This course introduces the concept of Machine Learning, commonly used Machine Learning algorithms, and the available tools using Python libraries such as NumPy, SciPy (Scikit-learn), and Panda. Different types of learning algorithms including supervised learning, unsupervised learning, and reinforcement learning are discussed. Some common Machine Learning algorithms are examined in applications with example problems – training the data, finding a model, and making predictions. Practices are done in Python coding. Other topics covered are data visualization, training/testing data and making predictions from the model, model evaluation and parameter tuning.

Prerequisite Tree

  • CS218 — Data Structure and Algorithms
    • CS210 — Programming Languages
      • IT145 — Foundation in Application Development
        • CS110 — Fundamentals of Programming
        • IT140 — Introduction to Scripting
    • CS217 — Object Oriented Programming
      • CS113 — Introduction to Programming
      • CS113L — Introduction to Programming Lab
      • IT145 — Foundation in Application Development
        • CS110 — Fundamentals of Programming
        • IT140 — Introduction to Scripting
    • MAT230 — Discrete Mathematics
    • MAT239 — Mathematics for Computing
  • MAT350 — Applied Linear Algebra
    • MAT225 — Calculus I: Single-Variable Calculus
      • MAT140 — Precalculus
        • MAT136 — Introduction to Quantitative Analysis
      • MAT142 — Precalculus with Limits

Interactive Prerequisite Graph

Unofficial — For Informational Purposes Only

This site is unofficial and is intended for informational purposes only. Course requirements, transfer evaluations, catalog rules, and program requirements can change. Always confirm your academic plan with your SNHU advisor for official guidance.