Welcome to cse142-notes’s documentation!
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Contents:
Introduction
Main Steps
Feature Extraction
Training
Testing
Supervised Learning
Classification
Regression
Ranking
More
Other Forms
Training
Loss Function
Stochastic Gradient Descent
Choices
Evaluation
Accuracy
Confusion Matrix
Precision, Recall, F-measure
Reporting Performance
Regressions
Least Square
Criterion
SGD
Closed Form
Maximum Likelihood
Convexity and Gradients
Bias-Variance Decomposition
Regularized Least Squares
Logistic Regression
Likelihood
Multiclass
Probability Review
Bayes Rule for Learning
Bayes’ Estimation
Maximum Likelihood
Maximum a Posteriori
Predictive Distribution
Mean a’Post
Use
Compound Experiment
Generative and Discriminative Models
Instance-Based Learning
Nearest Neighbor
K-d Trees
Noise
K-Nearest Neighbors
Nonparametric Regression
Naive Bayes
Issues
NB for Text
Perceptron
Linear Boundary
Prediction
Training
Caveats
Example
Convergence
Proof
Linear Models
Minimum Error Hyperplane
Alternatives to 0-1 Loss
Sub-gradient Descent
Decision Trees
Algorithm
“Best”
Example
Entropy
Information Gain
Overfitting
Pruning
Random Forests
Continuous Attributes
Missing Values
Other Issues
SVMs
Max-Margin Classification
Hard SVM
Setup
Prediction
Intuition
Optimization
Soft-Margin SVMs
Discussion
Hinge Loss
Solving
Hard-Margin SVM
Dual Form
Derivation
Non-Linearly-Seperable
Feature Mapping
Kernel Methods
Kernel in SVM
Formal Definition
Perceptron
Unsupervised Learning
Algorithm
Convergence
Initialization
Furthest-First
How to choose K
Probabilistic Clustering
Setup
Params from Assignments
Assignments from Params
PCA
First View: Preserve Variance
Second View: Data Compression
Uses
EM/GMM
Log-Likelihood
Iteratively
Ensemble/Boosting
Ensemble Methods
Creation
Prediction
Boosting
Ada-Boost
Analyzing Error
Proof
Discussion
Margin Approach
Neural Nets
Activations
Training
VAE
KL Divergence
Random
f-GAN
Indices and tables
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Contents:
Introduction
Regressions
Probability Review
Instance-Based Learning
Naive Bayes
Perceptron
Linear Models
Decision Trees
SVMs
Kernel Methods
Unsupervised Learning
PCA
EM/GMM
Ensemble/Boosting
Neural Nets
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