Machine Learning

Course Offering

Course Overview

Course Objective

Course Syllabus

  1. Introduction: Introduction to Machine Learning; Deep Learning vs Machine Learning; Review
  2. Empirical Risk Minimization: Learning Problems and the Risk Minimization Framework
  3. Regression: Linear and Logistic Regression
  4. Machine Learning in Practice: Bias-Variance; Training/Testing; Overfitting; Jackknifing/Cross-Validation; Occam’s razor; Regularization and Model Selection
  5. Classification: Naïve Bayes; Decision Trees; Random Forests
  6. Neural Networks: Perceptron; Backpropagation; Training Neural Networks
  7. Support Vector Machines: Optimization basics; Primal Form; Dual Form; Kernel SVMs, Soft-Margin SVMs
  8. Unsupervised Learning: k-means clustering; Spectral Clustering (if time permits)
  9. Deep Learning: Convolutional Neural Networks and applications to Images and Speech
  10. Ethical Issues: Fairness, Accountability, and Transparency in Machine Learning


Related Journals and Conferences [!Exhaustive]

Course Administration

Plagiarism Policy

Auditing Requirements