Calendar and Class Notes

   Topics

Course overview, introduction to machine learning, real-world applications and impacts, cognitive science applications

Ch 1. Introduction  (K. Murphy)

Ch 1. Introduction  (Duda et al.) 

Vectors  (by 3Blue1Brown)

Linear transformations and matrices  (by 3Blue1Brown)

UC Irvine ML: Introduction  

(Alexander Ihler) 

Review of linear algebra and vector calculus

Part I.2 Linear Algebra  (Goodfellow et al.)

Data formulation and problem definition 

UC Irvine ML: Data 

(Alexander Ihler)

UC Irvine ML: Probability 

(Alexander Ihler) 

Vector

UC Irvine ML: Supervised Learning 

(Alexander Ihler) 

Decision boundary

Decision boundary

Decision stump classifier

UC Irvine ML: Linear Classifier

(Alexander Ihler)

Estimation

Convexity

Vector Calculus

UC Irvine ML:  Complexity and overfitting

(Alexander Ihler) 

Linear Regression

UC Irvine ML: Linear regression

(Alexander Ihler) 

Midterm I

Linear Regression and Robust Estimation

UC Irvine ML: Gradient Descent

(Alexander Ihler) 

Linear Regression

Neural Networks  (3Blue1Brown) 

Error Metrics

Logistic Regression

No class

Regularization and Overfitting  (Andrew Ng) 

Logistic regression classifier

Support Vector Machine

 UC Irvine ML: Duals

(Alexander Ihler) 

Midterm II

SVM and Kernels

Cross-validation

Thanksgiving

Nearest neighbor

Decision tree

Ensemble classifier: boosting, random forest