Machine Learning is the subfield of the artificial interlligence that give computers the ability to learn without being explicityly programmed.¹

The main types of machine learning are supervised learning and unsupervised learning.

Supervised Learning #

Supervised Learning learns from being given “right answers” has been used most widely in real-world applications including spam filtering, speech recoginition, machine translation, online adversising, self-driving cars and visual inspection. The two major algorithms in supervised learning are regression and classification.

Regression #

Regression predict a number infinitely many possible outputs, such as inferring the price of a house from a given housing price dataset. The algorithm used here is a typical type of Linear Regression. The terminology of the dataset should trainning set, where x represents the input variable and y represents the output variable.

Here is a summary of some of the notation you will encounter.

|General
Notation | Description| Python (if applicable) | |: ————|: ————————————————————|| | $a$ | scalar, non bold || | $\mathbf{a}$ | vector, bold || | Regression | | | | | $\mathbf{x}$ | Training Example feature values (in this lab - Size (1000 sqft)) | x_train |
| $\mathbf{y}$ | Training Example targets (in this lab Price (1000s of dollars)) | y_train | $x^{(i)}$, $y^{(i)}$ | $i_{th}$Training Example | x_i, y_i| | m | Number of training examples | m| | $w$ | parameter: weight | w | | $b$ | parameter: bias | b |
| $f_{w,b}(x^{(i)})$ | The result of the model evaluation at $x^{(i)}$ parameterized by $w,b$: $f_{w,b}(x^{(i)}) = wx^{(i)}+b$ | f_wb |

Cost Function #

• Cost Function provides a measure of how well your predictions match your training data. The equation for cost with one variable is:$$J(w,b) = \frac{1}{2m} \sum\limits_{i = 0}^{m-1} (f_{w,b}(x^{(i)}) - y^{(i)})^2 \tag{1}$$. If the $(f_{w,b}(x^{(i)}) - y^{(i)})^2 $ term will be zero, then the cost is minimized, indicating that the best parameters fit the model well.

• Visulization of Cost Function Both 3D plots and contour plots can visualize a cost function to observe if the corresponding parameters fit the data well. To automate the process of optimizing w and b, we should use gradient descent.

-gradient descent

• Classification predict categories and small finite limited numbers.

Unsupervised Learning find something interesting in unlabeled data. The data comes with input x, but without labels y.

• Clustering is used to group similar data points like google news.
• Anomaly detection is for finding unusual data points like fraud detections.
• Demensionality reduction compress data using fewer numbers.

Multipile linear regression

Last modified on 2024-07-05