Topic
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Date
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Notes
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Linear modeling
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Background
Linear models
Least squares notes
Least squares gradient descent
algorithm
Regularization
Stochastic gradient descent pseudocode
Stochastic gradient
descent (original paper)
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Neural networks
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Multilayer perceptrons
Basic single hidden layer neural network
Back propagation
Approximations by superpositions of sigmoidal functions (Cybenko 1989)
Approximation Capabilities of Multilayer Feedforward Networks (Hornik 1991)
The Power of Depth for Feedforward Neural Networks (Eldan and Shamir
2016)
The expressive power of neural networks: A view from the width
(Lu et. al. 2017)
Convolution and single layer neural networks objective and optimization
Softmax and cross-entropy loss
Relu
activation single layer neural networks objective and optimization
Multi layer neural network objective and optimization.pdf
Image localization and segmentation
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Machine learning - running linear models in Python scikit-learn
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Scikit learn linear models
Scikit learn support vector machines
SVM in Python scikit-learn
Breast cancer training
Breast cancer test
Linear data
Non linear data
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Cross validation and balanced accuracy
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Cross validation
Training vs. validation accuracy
Balanced error
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Deep learning - running neural networks in Scikit-learn
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Scikit-learn MLPClassifier
Scikit-learn MLP code
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Multiclass classification - linear models and neural networks
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Multiclass classification
Different multiclass methods
One-vs-all method
Tree-based multiclass
Multiclass neural network
softmax objective
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Deep learning - running neural networks in Keras on tabular data
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Categorical variables
One hot encoding in scikit-learn
Keras
multilayer perceptron on tabular data
Keras
multilayer perceptron on tabular data with feature spaces
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Convolutions and image classification
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Image classification code
Convolutions
Popular convolutions in image
processing
Convolutions (additional notes)
Convolutions - example 1
Convolutions - example 2
Convolutions - example 3
Convolutions - example 4
Popular convolutions in image
processing
Convolutional neural network
(Additional slides by Yunzhe Xue)
Convolution and single layer neural networks objective and optimization
Training and designing
convolutional neural networks
Flower image classification with CNNs code
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Neural networks gradient descent, optimization,
batch normalization, common architectures, data augmentation
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Optimization in neural networks
Stochastic gradient descent pseudocode
Stochastic gradient
descent (original paper)
Image classification code v2
Batch normalization
Batch normalization paper
How does batch normalization help optimization
Gradient descent optimization
An overview of gradient descent optimization algorithms
On training deep networks
The Loss Surfaces of Multilayer Networks
Common architectures
Transfer learning by Yunzhe Xue
Transfer learning in Keras
Pre-trained models in Keras
Understanding data augmentation
for classification
SMOTE: Synthetic Minority
Over-sampling Technique
Dataset Augmentation in Feature
Space
Improved Regularization of
Convolutional Neural Networks with Cutout
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Logistic regression
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Logistic regression
Softmax and cross-entropy loss
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Feature selection
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Feature selection
Feature selection (additional notes)
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Clustering
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Clustering
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Exam review sheet
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Exam review sheet
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Dimensionality reduction
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Dimensionality reduction
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Decision trees and random forests
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Decision trees, bagging, boosting, and stacking
Decision trees (additional notes)
Ensemble methods (additional notes)
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Kernels (time permitting)
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Kernels
More on kernels
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Maximum likelihood (time permitting)
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Bayesian learning
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Autoencoders (time permitting)
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Generative models and networks
Autoencoder
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