ECE365: Fundamentals of Machine Learning (Lectures)
You can find the typed notes for this class [here]. They will be updated as needed (with a changelog below). The course follows essentially linearly with the notes.
The links to each lecture's in class (handwritten) notes are given below.
Lecture 1  Introduction to the course; Review of linear algebra and probability  [notes] 
Lecture 2  kNearest Neighbor Classifiers and Bayes Classifiers  [notes] 
Lecture 3  Linear Classifiers and Linear Discriminant Analysis  [notes] 
Lecture 4  Naive Bayes and Kernel Tricks  [notes] 
Lecture 5  Logistic Regression, Support Vector Machines and Model Selection  [notes] 
Lecture 6  Kmeans Clustering  [notes] 
Lecture 7  Linear Regression  [notes] 
Lecture 8  EigenDecomposition  [notes] 
Lecture 9  SVD  [notes] 
Lecture 10  PCA and wrap up  [notes]

