# An introduction to the basic terminology and notations

Now that we have discussed the three broad categories of machine learning—supervised, unsupervised, and reinforcement learning—let us have a look at the basic terminology that we will be using in the next chapters. The following table depicts an excerpt of the *Iris* dataset, which is a classic example in the field of machine learning. The Iris dataset contains the measurements of 150 iris flowers from three different species: *Setosa*, *Versicolor*, and *Viriginica*. Here, each flower sample represents one row in our data set, and the flower measurements in centimeters are stored as columns, which we also call the features of the dataset:

To keep the notation and implementation simple yet efficient, we will make use of some of the basics of *linear algebra*. In the following chapters, we will use a *matrix* and *vector* notation to refer to our data. We will follow the common convention to represent each sample as separate row in a feature matrix , where each feature is stored as a separate column.

The Iris dataset, consisting of 150 samples and 4 features, can then be written as a matrix :

### Note

For the rest of this book, we will use the superscript *(i)* to refer to the *i*th training sample, and the subscript *j* to refer to the *j*th dimension of the training dataset.

We use lower-case, bold-face letters to refer to vectors and upper-case, bold-face letters to refer to matrices, respectively . To refer to single elements in a vector or matrix, we write the letters in italics ( or , respectively).

For example, refers to the first dimension of flower sample 150, the *sepal width*. Thus, each row in this feature matrix represents one flower instance and can be written as four-dimensional column vector , .

Each feature dimension is a 150-dimensional row vector , for example:

.

Similarly, we store the target variables (here: class labels) as a 150-dimensional column vector .