Probabilistic Machine Learning: An Introduction Book Club
Welcome
Book club meetings
Pace
1 Foundations
Origin of the book
Python packages
Notebooks for the book
What is Machine Learning
Types of Machine Learning Problem
Types of ‘uncertainty’
Meeting Videos
Cohort 1
9 Linear Discriminant Analysis
Tooling
Classification Models
Linear discriminant analysis
Gaussian discriminant analysis
Gaussian discriminants -> Quadratic decision boundaries
Tied covariance matrices -> Linear decision boundaries
LDA & Logistic regression
Naive Bayes Example
Data: Palmer Penguins
Motivation
One Categorical Predictor
Recall: Bayes Rule
Calculation
One Numerical Predictor
Prior Probability Distributions
Two Predictor Variables
Naive Bayes in R
Models
Predictions
Validation
Confusion Matrices
Cross-Validation
NBC Math
MLEs
Imputation
Bayes and Logistic Regression
Naive Bayes
Logistic Regression
Covariance Revisited
FLDA Ideas
PCA Example
FLDA Example
FLDA vs PCA
Discriminative vs Generative
Advantages of discriminative classifiers
Advantages of generative classifiers
Meeting Videos
Cohort 1
10 Logistic Regression
Logistic Regression
Logistic Regression in R
Palmer Penguins Example
Generalized Linear Models
Robust Logistic Regression
Bi-Tempered Loss
Robust Logistic Regression in R
Bayesian Logistic Regression
Laplace Approximation
MCMC
Prior Distribution
Posterior Distribution
Probit Approximation
Approximation of Posterior
Probit Approximation in R
Summary
Meeting Videos
Cohort 1
11 Linear Regression
SLIDE 1
Meeting Videos
Cohort 1
12 Generalized Linear Models
SLIDE 1
Meeting Videos
Cohort 1
13 Neural Networks for Structured Data
SLIDE 1
Meeting Videos
Cohort 1
14 Neural Networks for Images
SLIDE 1
Meeting Videos
Cohort 1
15 Neural Networks for Sequences
SLIDE 1
Meeting Videos
Cohort 1
Published with bookdown
Probabilistic Machine Learning: An Introduction Book Club
11 Linear Regression
Learning objectives:
THESE ARE NICE TO HAVE BUT NOT ABSOLUTELY NECESSARY