Watch this episode of Crash Course Computer Science, hosted by Carrie Anne Philbin.

In this episode of Crash Course Computer Science, Carrie Anne Philbin offers a high-level overview of machine learning and walks us through an example of a classification problem

A few key points:

• Supervised ML classifiers distill the complexity of real-world objects and phenomena into features: values that usefully characterize the things we wish to classify (see our simple classifier review below!)

• The goal of ML classifiers is to maximize correct classifications while minimizing incorrect classifications.

• Several ML techniques are largely rooted in the field of statistics and are built upon statistical frameworks.

• A common ML methodology is the use of artificial neural networks (NN or neural nets). Neural nets are comprised of artificial neurons (inspired by biological neurons) which are organized into a series of layers (input, hidden, and output).

  • • In a classification scenario, neural nets can process inputs and propagate them forward through a series of weighting, summing, biasing, and applying activation functions (for more complex/nonlinear decision boundaries) to reach a classification decision.

Watch this episode of Crash Course Artificial Intelligence, hosted by Jabril Ashe.

A few key points:

• Supervised learning is the sect of ML which uses labeled data to train a model

• Rosenblatt’s Perceptron was the first algorithmically described supervised neural network whose conceptual underpinning continues to be used in supervised binary classification.

• In a supervised binary classifier that uses a step function, the classifier receives inputs that are multiplied by their respective weights and compared to an adjustable threshold weight called the "bias." If the sum of these multiplied inputs is less than the bias, then the neuron outputs a 0. If the sum is greater than the bias, then the neuron will output a 1.

  • • If the classification is correct, weights and biases are not updated. If the classification is incorrect, weights and biases are updated, which also updates the decision boundary.

Watch this episode of Crash Course Artificial Intelligence, hosted by Jabril Ashe.

A few key points:

• Unsupervised learning is the sect of ML which uses unlabeled data to train a model.

• Computational recognition of shared properties among instances in a dataset is called unsupervised clustering.

• K-means is one of the simplest unsupervised clustering algorithms.

  • • K-means relies on the following assumptions:

      • • There are k number of clusters in a dataset based on pre-selected properties of interest

        • There is a way to compare observations

        • There is a way to surmise how many clusters exist in a dataset based on pre-selected properties

        • The mean is a valid measure of central tendency to create centroids for each predicted cluster

A great thread about k-means algorithm assumptions can be seen here.

Watch this episode of Crash Course Statistics, hosted by Adriene Hill.

A few key points:

• Expanding on K-means:

  • • The first set of k clusters are determined by arbitrarily placing k centroids and treating them as the center of each cluster.

    • Each data point is assigned to the cluster of the centroid it is closest to (based on pre-selected properties).

    • The centroid (mean) is recalculated based on the datapoints now contained in each cluster and the data points are again reassigned.

    • The process of centroid recalculation and cluster reassignment continues until the centroids converge (i.e., the centroids and clusters stop changing).

• Because unsupervised learning does not use labels, there are no "true" values to compare to, so we are unable to use a confusion matrix to assess unsupervised clustering performance like we can for supervised classification. However, we can use other metrics, like the silhouette score to assess cluster cohesion and separation.

• Another type of clustering often used in ML is hierarchical clustering.

  • • Hierarchical clustering is used to create a hierarchy of clusters in an unlabeled dataset such that a tree-shaped structure (a dendrogram) is formed.

      • • The "similarity" of clusters is determined by how far up in the dendrogram they join.

Review this mini-lecture offered by D. Sculley from Google's Machine Learning Crash Course.

In this mini-lecture, D. Sculley walks us through a real-world example of how label leakage impacted the generalizability of a cancer prediction model (we'll cover model generalization in Module 2).

Label leakage (also called data leakage, target leakage, or just leakage) occurs when targetted predictions are inadvertently present in a training dataset, resulting in excellent model performance on test data, but poor performance on new data.

A few key points:

• A cancer prediction model was trained using data from patient medical records with features of interest including patient age, gender, prior conditions, hospital name, vital signs, test results, etc.

• The model gave excellent performance on withheld test data; however, its performance suffered when using new patient data.

• The reason for this was that one feature, hospital name (which included strings like "Beth Israel Cancer Center"), was an obviously reliable indicator for whether a patient had cancer (i.e., patients being treated at cancer centers are far more likely to have cancer than those who are not). Using this feature was a subtle way for the model to "cheat."

  • • NOTE: Even if the string itself did not contain the word "cancer" or if the hospital feature was an anonymized integer, label leakage would still occur due to undiscovered feature correlation (high cancer patient correlation with cancer treatment centers).