I started the machine learning course offered by Stanford on YouTube by Andrew Ng, but I quickly learned that I needed some math refresher if I wanted to really understand what he was talking about. Here’s my learning plan that I hope to follow to complete that machine learning course and beyond (all courses are available on YouTube):

  • Calculus [Khan Academy]
  • Differential Equations [Khan Academy]
  • Linear Algebra [Khan Academy]
  • Probability and Statistics [Khan Academy]
  • Statistical Aspects of Data Mining (stats 202) [GoogleTalks]
  • 18.03 Differential Equations [MIT OCW]
  • 6.041 Probabilistic Systems Analysis and Applied Probability [MIT OCW]
  • 6.262 Discrete Stochastic Processes [MIT OCW]
  • 18.06 Linear Algebra [MIT OCW]
  • RES.6.007 Signals and Systems [MIT OCW]
  • The Fourier Transform and its Applications [Stanford Engineering Everywhere]
  • Introduction to Linear Dynamical Systems [Stanford Engineering Everywhere]
  • Convex Optimization I [Stanford Engineering Everywhere]
  • Convex Optimization II [Stanford Engineering Everywhere]
  • Stanford AI- Machine Learning [Stanford Engineering Everywhere]
  • CalTech Machine Learning Lecture 1 [CalTech YouTube channel]
  • 2.830J, Control of Manufacturing Processes S08 [MIT OCW]
  • 16.885J Aircraft Systems Engineering [MIT OCW]
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