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CPS 803 CMF

CP 8318 CMF

 

CPS 803 / CP 8318 - Machine Learning

Fall 2019 Semester

Instructor: Neil Bruce
bruce@ryerson.ca
Office: ENG-265
Time: W 10:00-1:00 pm
Location: TRS 2166

 
 

Announcements

Assignment 1 may be found at this link: Assignment 1
Assignment 2 appears at this link: Assignment 2
Course Project requirements appear here: Project
Term test review materials appear at this link: Term Test Review

Lectures

 

 

Topics

Description

 

Introduction

Course overview, motivation and Math

Linear models

Linear regression, least-squares, bias-variance tradeoff, overfitting

Dealing with Data

Sourcing data, pre-processing, value of data, dimensionality, normalization

Non-Parametric Methods

Clustering, Example based classification, kNN

Regression vs. Classification

Classification as regression, logistic regression, SVMs, the kernel trick

Ensembles

Boosting and bagging, decision trees, naive Bayes, random forests

Neural Networks I

Perceptrons, Multi-layer perceptrons, Autoencoders

Neural Networks II

Convolutional Neural Networks, Recurrent neural networks, GANs

Evaluation

Performance evaluation, precision-recall, decision theory, accuracy, practial considerations

Statistical Models

Probability distributions, maximum likelihood, MAP, parameter estimation, mixtures of Gaussians, EM algorithm

Graphical Models

Bayes Nets, Markov Chains, Markov Random Fields, Hidden Markov Models, Sequence classification