# 1. Machine Learning - Giving Computers the Ability to Learn from Data

Overview:

Intro to three different types of machine learning:

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Notebooks:

# 2. Training Machine Learning Algorithms for Classification

Overview:

• Perceptron
• the activation function $\phi (z)$ is a simple unit step function, which is sometimes also called the Heaviside step function:
• The key difference between the Adaline rule (also known as the Widrow-Hoff rule) and Rosenblatt’s perceptron is that the weights are updated based on a linear activation function rather than a unit step function like in the perceptron. In Adaline, this linear activation function $\phi (z)$ is simply the identity function of the net input so that $\phi(w^Tx)= w^Tx$.
• While the linear activation function is used for learning the weights, a quantizer, which is similar to the unit step function that we have seen before, can then be used to predict the class labels.

Notebooks:

Code:

# 3. A Tour of Machine Learning Classifiers Using Scikit-Learn

Overview:

• Logistic regression intuition and conditional probabilities
• odds ratio: which is the odds in favor of a particular event,

$\frac{p}{1 - p}$, where p stands for the probability of the positive (1− p) event.

• logit function: the logarithm of the odds ratio (log-odds), $logit(p) = \log \frac{p}{1 - p}$

• Learning the weights of the logistic cost function
• Tackling overfitting via regularization
• Large scale machine learning and stochastic gradient descent

Notebooks:

Code: