1. Black Box Machine Learning
With the abundance of well-documented machine learning (ML) libraries, it's fairly straightforward for a programmer to "do" ML, without any understanding of how things are working. And we encourage this "black boxes" machine learning! (At least to start.) However, to make proper use of these ML libraries, one needs to be conversant in the basic vocabulary, concepts, and workflows that underlie ML. We'll introduce the standard ML problem types (classification and regression) and discuss prediction functions, feature extraction, learning algorithms, performance evalution, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.
This lecture may be safely skipped by those already familiar with practical machine learning.
Access the full course at https://bloom.bg/2ui2T4q
With the abundance of well-documented machine learning (ML) libraries, it’s fairly straightforward for a programmer to “do” ML, without any understanding of how things are working. And we encourage this “black boxes” machine learning! (At least to start.) However, to make proper use of these ML libraries, one needs to be conversant in the basic vocabulary, concepts, and workflows that underlie ML. We’ll introduce the standard ML problem types (classification and regression) and discuss prediction functions, feature extraction, learning algorithms, performance evalution, cross-validation, sample bias, nonstationarity, overfitting, and hyperparameter tuning.
This lecture may be safely skipped by those already familiar with practical machine learning.
Access the full course at https://bloom.bg/2ui2T4q
