Machine learning is one of today's most exciting emerging technologies.
In this course, you will learn what machine learning is, what are the most important techniques in machine learning, and how to apply them to solve problems in the real world.
We hear a lot about machine learning (or ML for short) in the news.
But what is it, really?
You use machine learninng every day when you run a search engine query.
Machine learning also powers the speech recognition, question answering and other intelligent capabilities of smartphone assistants like Apple Siri.
Machine learning is used in every spam filter, such as in Gmail.
ML systems are also used by credit card companies and banks to automatically detect fraudulent behavior.
One of the most exciting and cutting-edge uses of machine learning algorithms are in autonomous vehicles.
In 1959, Arthur Samuel defined machine learning as follows.
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed.
What does "learn" and "explicitly programmed" mean here? Let's look at an example.
A self-driving car system uses dozens of components that include detection of cars, pedestrians, and other objects.
One way to build a detection system is to write down rules.
# pseudocode example for a rule-based classification system object = camera.get_object() if object.has_wheels(): # does the object have wheels? if len(object.wheels) == 4: return "Car" # four wheels => car elif len(object.wheels) == 2:, if object.seen_from_back(): return "Car" # viewed from back, car has 2 wheels else: return "Bicycle" # normally, 2 wheels => bicycle return "Unknown" # no wheels? we don't know what it is
In practice, it's almost impossible for a human to specify all the edge cases.
The machine learning approach is to teach a computer how to do detection by showing it many examples of different objects.
No manual programming is needed: the computer learns what defines a pedestrian or a car on its own!
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. (Arthur Samuel, 1959.)
This principle can be applied to countless domains: medical diagnosis, factory automation, machine translation, and many more!
Why is this approach to building software interesting?
Machine learning is broadly defined as the science of building software that has the ability to learn without being explicitly programmed.
How might we enable machines to learn? Let's look at a few examples.
The most common approach to machine learning is supervised learning.
Consider a simple dataset for supervised learning: house prices in Boston.
# We will load the dataset from the sklearn ML library from sklearn import datasets boston = datasets.load_boston()
We will visualize two variables in this dataset: house price and the education level in the neighborhood.
import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = [12, 4] plt.scatter(boston.data[:,12], boston.target) plt.ylabel("Median house price ($K)") plt.xlabel("% of adults in neighborhood that don't have a high school diploma") plt.title("House prices as a function of average neighborhood education level")
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We can use this dataset of examples to fit a supervised learning model.
import numpy as np from sklearn.kernel_ridge import KernelRidge # Apply a supervised learning algorithm model = KernelRidge(alpha=1, kernel='poly') model.fit(boston.data[:,], boston.target.flatten()) predictions = model.predict(np.linspace(2, 35)[:, np.newaxis]) # Visualize the results plt.scatter(boston.data[:,], boston.target, alpha=0.25) plt.plot(np.linspace(2, 35), predictions, c='red') plt.ylabel("Median house price ($K)") plt.xlabel("% of adults in neighborhood that don't have a high school diploma") plt.title("House prices as a function of average neighborhood education level")
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Many of the most important applications of machine learning are supervised:
Here, we have a dataset without labels. Our goal is to learn something interesting about the structure of the data:
Here is a simple example of an unsupervised learning dataset: Iris flowers.
# Load and visualize the Iris flower dataset iris = datasets.load_iris() plt.scatter(iris.data[:,0], iris.data[:,1], alpha=0.5) plt.ylabel("Sepal width (cm)") plt.xlabel("Sepal length (cm)") plt.title("Dataset of Iris flowers")
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We can use this dataset of examples to fit an unsupervised learning model.
# fit a Gaussian Mixture Model with three components from sklearn import mixture model = mixture.GaussianMixture(n_components=3, covariance_type='full') model.fit(iris.data[:,[0,1]])
# display learned probabilities as a contour plot x, y = np.linspace(4.0, 8.0), np.linspace(2.0, 4.5) X, Y = np.meshgrid(x, y) Z = -model.score_samples(np.array([X.ravel(), Y.ravel()]).T).reshape(X.shape) plt.contour(X, Y, Z, levels=np.logspace(0, 10, 1), cmap="gray", alpha=0.5) plt.scatter(iris.data[:,0], iris.data[:,1], alpha=0.5) plt.scatter(model.means_[:,0], model.means_[:,1], marker='D', c='r') plt.ylabel("Sepal width (cm)") plt.xlabel("Sepal length (cm)") plt.title("Dataset of Iris flowers") plt.legend(['Datapoints', 'Probability peaks'])
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CS = plt.contour(X, Y, Z, levels=np.logspace(0, 30, 1), cmap='gray', alpha=0.5) p1 = plt.scatter(iris.data[:,0], iris.data[:,1], alpha=1, c=iris.target, cmap='Paired') plt.scatter(model.means_[:,0], model.means_[:,1], marker='D', c='r') plt.ylabel("Sepal width (cm)") plt.xlabel("Sepal length (cm)") plt.title("Dataset of Iris flowers") plt.legend(handles=p1.legend_elements(), labels=['Iris Setosa', 'Iris Versicolour', 'Iris Virginica'])
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Unsupervised learning also has numerous applications:
In reinforcement learning, an agent is interacting with the world over time. We teach it good behavior by providing it with rewards.
Image by Lily Weng
Applications of reinforcement learning include:
Machine learning is often discussed in the context of these two fields.
Next, let's look at the machine learning topics that we will cover.
The focus of this course is on applied machine learning.
Why are we following this approach?
You will use Python and popular machine learning libraries such as:
scikit-learn. It implements most classical machine learning algorithms.
pytorch. Standard libraries for modern deep learning.
pandas. Linear algebra and data processing libraries used to implement algorithms from scratch.
The core materials for this course (including the slides!) are created using Jupyter notebooks.
import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, neural_network plt.rcParams['figure.figsize'] = [12, 4]
We can use these libraries to load a simple datasets of handwritten digits.
# https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html # load the digits dataset digits = datasets.load_digits() # The data that we are interested in is made of 8x8 images of digits, let's # have a look at the first 4 images. _, axes = plt.subplots(1, 4) images_and_labels = list(zip(digits.images, digits.target)) for ax, (image, label) in zip(axes, images_and_labels[:4]): ax.set_axis_off() ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') ax.set_title('Label: %i' % label)
We can now load and train this algorithm inside the slides.
np.random.seed(0) # To apply a classifier on this data, we need to flatten the image, to # turn the data in a (samples, feature) matrix: data = digits.images.reshape((len(digits.images), -1)) # create a small neural network classifier from sklearn.neural_network import MLPClassifier classifier = MLPClassifier(alpha=1e-3) # Split data into train and test subsets X_train, X_test, y_train, y_test = sk.model_selection.train_test_split(data, digits.target, test_size=0.5, shuffle=False) # We learn the digits on the first half of the digits classifier.fit(X_train, y_train) # Now predict the value of the digit on the second half: predicted = classifier.predict(X_test)
We can now visualize the results.
_, axes = plt.subplots(1, 4) images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted)) for ax, (image, prediction) in zip(axes, images_and_predictions[:4]): ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest') ax.set_title('Prediction: %i' % prediction)
We will go over some practical bits of information.
The format of this course will be that of the "reverse classroom".
The course spans about 25 lectures approximately divided up into a set of blocks:
This course is designed to aimed at a very general technical audience. Main requirements are: