ECE 6254 - Statistical Machine Learning

Resources

 

• A short, useful introduction to Matrix Calculus
• The Matrix Cookbook
• Khan Academy lectures on probability
• Khan Academy lectures on linear algebra
• Machine learning as taught by Andrew Ng
• Machine learning as taught by Hal Daumé
• Machine learning as taught by Yaser Abu-Mostafa (the Caltech course based on Learning from Data)
• Talking machines: An interesting podcast about machine learning

 

Python Resource

• Anaconda Python distribution
• Official Python documentation
• Google's Python class
• Another Python tutorial
• SciPy tutorial
• NumPy for Matlab users
• Python quick reference sheet
• scikit-learn
• Python for Data Analysis, McKinney, 2012. (Amazon)

 

General Interest

• Taking Tests
• Careers in Signal Processing

Extra Reading and Practice

 

Practice Problems

• Midterm practice problems
• Some linear algebra and other problems from ECE 6250
  • The linked problem sets can be found here (the links on the 6250 site are behind a wall).  Also, when not specified, the text links are to Moon & Stirling's Mathematical Methods and Algorithms for Signal Processing .
    • Chapter 4 Problems
    • Chapter 5 Problems
    • Chapter 6 Problems
    • Chapter 7 Problems
    • Chapter 8 Problems

 

Papers

• Introduction to Statistical Learning Theory, Bousquet, Boucheron, and Lugosi, 2004.

 

 

Books on statistical learning

 

 

 

 

Books covering background material

• How to Solve It, Polya, 1945. Classic introduction to mathematical problem solving. (Amazon)
• Introduction to Probability, Bertsekas and Tsitsiklis, 2008. Good introduction to elementary probability theory. (Amazon)
• An Introduction to Probability Theory and its Applications, Feller, 1950. An absolute classic. A more advanced (but still accessible) introduction to probability theory. (Amazon)
• The Art of Probability, Hamming, 1994. Another introduction to probability theory written by a fellow engineer. (Amazon)
• All of Statistics, Wasserman, 2004. Exactly what the title says. (Amazon)
• Linear Algebra and Its Applications, Strang, 2005. A great introduction to linear algebra. (Amazon)
• Convex Optimization, Boyd, 2004. We will cover the tools from convex optimization that we need as part of the course, but if you want to know more, this is a great resource targeted towards electrical engineers. (Free online version, Amazon)