Mlvu 1 2 Classification

Summary

In the final video of this lecture, we see how to apply these principles to Lecture 3 in the Machine Lecture course at the VU University Amsterdam. Lecturer: Peter Bloem. See the PDF for image credits. The basics of neural networks: perceptrons, nonlinearities, feedforward networks/MLPS and stochastic gradient descent. slides: ... Continuing our theme of pre-processing, we discuss how to deal with class imbalance, how to transform given data to features, ... In the first video of the first lecture, we answer the question of what machine learning is and when to use it. We look at some ... In this video, we review the basics of probability theory: subjective and objective probability. Sample spaces, event spaces, ... We finish up the lecture by briefly discussion validation in embedding models. We touch briefly on the difference between ... Day 8 of Harvey Mudd College Neural Networks class. In this lecture we look at the basics of linear regression: how to define a model, and how to define a loss function. slides: ...