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README.md

MCSC 6230/7230

Fall 2017, Modeling and Computational Science, Machine Learning Course.

Course web is available here http://csgrad.science.uoit.ca/courses/mcsc-ml/

Contents

Basics

  • Gaussian distribution in 2D
  • Gaussian distribution conditionals
  • Gaussian likelihood
  • Yet another 1D Gaussian
  • Sigmoid function

Regression

  • Linear regression using gradient descent
  • Linear regression (polynomials)
  • Linear regression using Keras
  • Linear regression using TensorFlow
  • Linear regression using Sklearn
  • Linear regression in 2D
  • Regularizers
  • Bayesian linear regression

Logistic Regression

  • Logistic regression
  • Softmax classification

Gaussian Processes

  • Gaussian Processes

Kernel Principle Component Analysis

  • Kernel PCA

Coding and such

  • Meshgrid
  • Numpy where

Tensorflow

  • Loading a variable
  • Linear regression
  • Logistic regression
  • RNN
  • Saving a variable
  • Graphviz
  • Convolutional neural network
  • Using a variable

Clustering

  • Agglomerative clustering
  • Kmeans image segmentation
  • Kmeans
  • Meanshift

RNN

  • Characters