Machine Learning

DA 220

Introduction to machine learning

Linear regression

Logistic regression

Bias-Variance trade-off

Regularization

Bayes Decision Theory

Unconstrained optimization: the basics

Naive Bayes

Linear Discriminant Function

KNN

Decision Trees

Bagging, Random Forests

Boosting, AdaBoost

K-means, K-medoids

Perceptron Learning

Multi-layer perceptron

Backpropagation

Training Deep Neural Networks: Optimization Algorithms

Training Deep Neural Networks: Dropout, Batch normalization

Training Deep Neural Networks: Exploding/Vanishing gradients

Training Deep Neural Networks: Weights initialization

TensorFlow

Convolutional Neural Networks

Recurrent Neural Networks

Encoder-Decoder Models

Principal Component Analysis

Support Vector Machines

Kernel Methods

Reference Books:

  • Pattern Recognition and Machine Learning, Springer

  • Elements of Statistical Learning, Springer

  • Machine Learning -- A Probabilistic Perspective, MIT Press

  • Deep Learning, MIT Press