19Fall CS6316 - Machine Learning



MoWe 2:00PM - 3:15PM @ MEC 205

2019 Fall UVa CS 6316 Machine Learning Lectures Organized by Given Order

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Introduction

1Basic

Algebra Review

1Basic

Linear Regression

2Regression Linear 1Basic

Optimization for LR

2Regression Optimization

Nonlinear Regression

2Regression Nonlinear ModelSelection Local

Linear Prediction with Regularization

2Regression Optimization Regularization ModelSelection

Lasso and Elastic Net

2Regression Optimization Regularization ModelSelection

Feature Selection and Model Selection

2Regression Optimization Regularization DimenReduct ModelSelection

supervised classification

1Basic

KNN and Theory

3Classification 5Theory Local ModelSelection

Bias Variance Tradeoff

5Theory Local

SVM

3Classification Linear Discriminative Regularization Optimization 5Theory

SVM, Kernel

3Classification Discriminative Regularization Optimization 5Theory

SVM, Dual

3Classification Discriminative Regularization Optimization 5Theory

ProbReview + MLE

1Basic

Logistic and NN

3Classification Nonlinear Deep Linear Discriminative

NN and Deep Learning

3Classification Nonlinear Deep Discriminative

NN and Deep Learning

3Classification Nonlinear Deep Discriminative

Quick survey of recent deep learning

3Classification Nonlinear Deep Discriminative 4Unsupervised Generative

Generative Classification

3Classification Generative

Gaussian BC

3Classification Generative

NaiveBC on Text

3Classification Generative

DecisionTree and Bagging

3Classification Ensemble 5Theory

RF and Boosting

3Classification Linear Discriminative

Clustering Hier

4Unsupervised

Clustering Partition

4Unsupervised Generative

Clustering GMM

4Unsupervised Generative 5Theory

Review

1Basic

Friday Final Project Presentations

1Basic
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