|
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 | k-Nearest 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 | K-means Clustering | [notes] |
| Lecture 7 | Linear Regression | [notes] |
| Lecture 8 | Eigen-Decomposition | [notes] |
| Lecture 9 | SVD | [notes] |
| Lecture 10 | PCA and wrap up | [notes]
|
|