Machine Learning

Course Code: 
EE 58A
Course Title: 
Machine Learning

The course is designed to give a wide perspective in machine learning, including but not limited to deep learning methods. The course will be online using notes on Jupyter Notebooks. The coding environment is Julia. The students are expected to install Julia and go over documentation on their own. The projects will also be coded in Julia and are expected to involve a comparative assessment of deep learning and at least one of other methods. Examples in Julia on selected topics are planned. 

A brief outline is as follows:

  1. What is Machine Learning
  2. Parametric Learning Basics,
  3. Regression Classics, eg. LS, Ridge, RLS
  4.  Classification Classics, eg. kNN, LR, LDA, SVM, RF, XGB
  5. Projection onto Convex Sets, eg. POCS
  6. Sparsity Promoting Learning, eg. LASSO
  7. Kernel Methods, eg. GLM, k-SVM
  8. Deep Learning
    1. The perceptron and feed-forward models
    2. Backpropagation
    3. Activation and loss functions
    4. Optimizers
    5. Main Architectures
  9. (Possibly) Manifold Learning and Latent Variables


This page is meant to provide general information. All interaction, including online courses, project submissions, etc. will be the course page on