# Resources

The primary resources for this course are shared below.

## Assignments

See Syllabus for more information.

## Lecture Slides

See Syllabus for more information.

- Lecture 1: Welcome Lecture and Linear Algebra Review

[Linear Algebra Review] [Introduction to Machine Learning] [Reading for Assignment 1] [Recording] - Lecture 2: Supervised Learning

[Supervised Learning] [Recording] [Reading: LIME] [Reading SHAP] - Lecture 3: Decision Trees

[Decision Trees Representation] [Decision Trees Learning] [Decision Trees Discussion] - Lecture 4: Neural Networks

[Neural Network Introduction] [Neural Network Forward Pass] [Neural Network Forward Pass Recording] [Neural Network Backward Pass] [Neural Network Backward Pass Recording] [Neural Network Backward Pass Recording 2] [Neural Network Practical Concerns] [Dropout] - Lecture 5: Computational Learning Theory with Neural Networks

[Bias and Variance] [Bias and Variance Recording] [Mistake Bound Theorem] [Mistake Bound Theorem Recording] [Perceptron Mistake Bound] [PAC Learning - 1] [PAC Learning - 2: Definition] [PAC Learning - 3: Occam Razor] [VC Dimensions] - Lecture 6: Bagging and Boosting

[Bagging and Boosting] - Lecture 7: Support Vector Machines

[SVM Introduction] [SVM Intro Recording] [Dual SVM]