Machine Learning
DA 220
Introduction to machine learning
Introduction to machine learning
Linear regression
Linear regression
Logistic regression
Logistic regression
Bias-Variance trade-off
Bias-Variance trade-off
Regularization
Regularization
Bayes Decision Theory
Bayes Decision Theory
Unconstrained optimization: the basics
Unconstrained optimization: the basics
Naive Bayes
Naive Bayes
Linear Discriminant Function
Linear Discriminant Function
KNN
KNN
Decision Trees
Decision Trees
Bagging, Random Forests
Bagging, Random Forests
Boosting, AdaBoost
Boosting, AdaBoost
K-means, K-medoids
K-means, K-medoids
Perceptron Learning
Perceptron Learning
Multi-layer perceptron
Multi-layer perceptron
Backpropagation
Backpropagation
Training Deep Neural Networks: Optimization Algorithms
Training Deep Neural Networks: Optimization Algorithms
Training Deep Neural Networks: Dropout, Batch normalization
Training Deep Neural Networks: Dropout, Batch normalization
Training Deep Neural Networks: Exploding/Vanishing gradients
Training Deep Neural Networks: Exploding/Vanishing gradients
Training Deep Neural Networks: Weights initialization
Training Deep Neural Networks: Weights initialization
TensorFlow
TensorFlow
Convolutional Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Recurrent Neural Networks
Encoder-Decoder Models
Encoder-Decoder Models
Principal Component Analysis
Principal Component Analysis
Support Vector Machines
Support Vector Machines
Kernel Methods
Kernel Methods
Reference Books:
Reference Books:
Pattern Recognition and Machine Learning, Springer
Elements of Statistical Learning, Springer
Machine Learning -- A Probabilistic Perspective, MIT Press
Deep Learning, MIT Press