Machine Learning | Linear Regression, Optimization, Regularization, Generalization and Extensions

Speakers:
Loh Ken Yaw
(Vincent) Ong Kok Rhui - Data Science student @ Monash University Malaysia

References:
Monash University Malaysia FIT5201 Machine Learning unit Week 3
Pattern Recognition and Machine Learning by Christopher Bishop
An Introduction to Statistical Learning in Python by Gareth James et. al

Timestamps:
00:00 Introduction to linear models for regression
02:03 Training error and Mean Squared Error (MSE)
03:36 Expected squared error and LOTUS principle
07:00 Central Limit Theorem and noise distribution
10:29 Basis functions and non-linear transformations
14:45 Optimizing parameters via partial derivatives
18:44 Gradient descent and stochastic gradient descent
23:53 Overfitting and training error vs generalization error
26:23 Regularization techniques to punish extreme weights
29:54 Ridge (L2) vs. Lasso (L1) regression
35:22 Simple and multiple linear regression overview
40:31 Estimating coefficients with the least squares approach
43:55 Assessing model accuracy and standard error of the mean
56:31 Hypothesis testing, null hypotheses, and p-values
1:02:09 Assessing model fit with RSE and R-squared statistics
1:09:57 Multiple linear regression and variable correlation
1:16:29 Using the F-statistic to test predictor relationships
1:23:48 Forward, backward, and mixed variable selection methods
1:29:50 Extending linear models and using interaction terms
1:40:20 Common issues and handling non-linearity in models

Видео Machine Learning | Linear Regression, Optimization, Regularization, Generalization and Extensions канала Kok Rhui