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"Software is eating the world"

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"AI is the new electricity"

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Machine Learning 101An introduction for developers

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Agenda

1. What is Machine Learning2. In practice: Python & its benefits3. Types of Machine Learning problems4. Steps needed to solve them5. How two classification algorithms work

a. K-Nearest Neighborsb. Support Vector Machines

6. Evaluating an algorithma. Overfitting

7. Demo8. (Very) basic intro to Deep Learning & what follows

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What is Machine Learning?

The subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959).

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E” (Tom Mitchell, 1997)

● Machine Learning vs AI?

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Some Python code

Why Python?

● Simple & powerful● Fast prototyping

○ Batteries included

● Lots of libraries○ For everything, not just Machine Learning○ Bindings to integrate with other languages

● Community○ Very active scientific community○ Used in academia & industry

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Data Science “Swiss army knife”

Data preparation / exploration / visualization

● Pandas● Matplotlib● Seaborn● Orange

Modeling / Machine Learning

● Scikit-learn● mllib (Apache Spark)

Text focused

● Gensim● nltk

Deep learning

● Keras● TensorFlow, Theano

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Types of ML problems

Supervised

● Learn through examples of which we know the desired output.

● Two types:○ Classification (discrete variables, ie. spam/no

spam)

○ Regression (continuous output, ie. temperature)

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Unsupervised

● Discover latent relationships in the data

● Two types:○ Dimensionality reduction (curse of

dimensionality)○ Clustering

Also have semi-supervised (use labeled and unlabeled data).

Steps in every Machine Learning problem

To solve any ML task, need to go through:

1. Data gathering2. Data processing & feature engineering3. Algorithm & training4. Applying model to make predictions (evaluate, improve)

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Data gathering

● Depends on the specific task● Might need to put humans to work :(

○ Ie. Manual labelling for supervised learning○ Domain knowledge. Maybe even experts.○ Can leverage Mechanical Turk / CrowdFlower.

● May come for free, or “sort of”○ Ie. Machine Translation.○ Categorized articles in Amazon, etc.

● The more the better○ Some algorithms need large amounts of data

to be useful (ie. neural networks).

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Data processing

Is there anything wrong with the data?

● Missing values● Outliers● Bad encoding (for text)● Wrongly-labeled examples● Biased data

○ Do I have many more samples of one class than the rest?

Need to fix/remove data?

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Feature engineering

What is a feature?

A feature is an individual measurable property of a phenomenon being observed

Our inputs are represented by a set of features. Eg:

● To classify spam email, features could be:○ Number of times some word appears (ie. pharmacy)○ Number of words that have been ch4ng3d like this.○ Language of the email (0=English, 1=Spanish, …)○ Number of emojis

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Buy ch34p drugs from the ph4rm4cy now :) :) :) :)

(1, 2, 0, 4)

Featureengineering

Feature engineering (2)

● Extract more information from existing data● Not adding “new” data per-se

○ Making it more useful○ With good features, most algorithms can learn faster

● It can be an art○ Requires thought and knowledge of the data

Two steps:

● Variable transformation (eg. dates into weekdays, normalizing)● Feature creation (eg. n-grams for texts, if word is capitalized to detect names, etc)

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Algorithm & training

Supervised

● Linear classifier● Naive Bayes● Support Vector Machines (SVM)● Decision Tree● Random Forests● k-Nearest Neighbors● Neural Networks (Deep learning)

Unsupervised / dimensionality reduction

● PCA● t-SNE● k-means● DBSCAN

They all understand vectors of numbers. Data are points in multi-dimensional space.

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k-Nearest Neighbors (k-NN)

Classification or regression.

Idea (classification):

● Choose a natural number k >= 1 (parameter of the algorithm)● Given the sample X we want to label:

○ Calculate some distance (ie. euclidean) to all the samples in the training set.○ Keep the k samples with the shortest distance to X. These are the nearest neighbors.○ Assign to X whatever class the majority of the neighbors have.

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K = 1 K = 5

k-Nearest Neighbors (k-NN) (2)

● Fast computation of nearest neighbors is an active area of research.● Naive brute-force approach computes distance to all points, need to keep entire

dataset in memory at classification time (no offline training).● Need to experiment to get the right k.

There are other algorithms that use approximations to deal with these inefficiencies.

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Support Vector Machines (SVM)

Idea:

● Separate data linearly in space.● There are infinite planes that can

separate blue & red dots.● SVM finds the optimal hyperplane to

separate the two classes (the one that leaves the most margin).

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Support Vector Machines (SVM) (2)

● SVMs focus only on the points that are the most difficult to tell apart (other classifiers pay attention to all the points).

● We call them the support vectors.● Decision boundary doesn’t change if we add more samples that are outside the

margin.● Can achieve good accuracy with fewer training samples compared to other

algorithms.

Only works if data is linearly separable.

● If not, can use a kernel to transform it to a higher dimension.

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Support Vector Machines: Kernel Trick

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Evaluating

● Split train / test set. Should not overlap!● Accuracy

○ What % of samples did it get right?

● Precision / Recall○ True Positives, True Negatives, False Positives, False Negatives○ Precision = TP / (TP + FP) (out of all the classifier labeled positive, % that actually was)○ Recall = TP / (TP + FN) (out of all the positive, how many did it get right?)○ F-measure (harmonic mean, 2 * Precision * Recall / (Precision + Recall))

● Confusion matrix● Many others

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Evaluating a spam classifier: example

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Id Is spam Predicted spam

1 Yes No

2 Yes Yes

3 No Yes

4 Yes Yes

5 No No

6 Yes No

7 No No

accuracy = 4/7 ~ 0.57

precision = 2/(2 + 1) = ⅔ ~ 0.667

recall = 2/(2 + 2) = ½ ~ 0.5

F-measure = 2*(2/3 * 1/2)/(2/3 + 1/2) = 4/7 ~ 0.57

Confusion matrix

Predicted spam

Predicted not spam

Spam 2 (TP) 2 (FN)

Not spam 1 (FP) 2 (TN)

Overfitting

● Models that adjust very well (“too well”) to the training data● It does not generalize to new data. This is a problem!

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Preventing overfitting

● Detect outliers in the data● Simple is better than complex

○ Fewer parameters to tune can bring better performance.○ Eliminate degrees of freedom. Eg. polynomial.○ Regularization (penalize complexity).

● K-fold cross validation (different train/test partitions)● Get more training data!

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Demo: scikit-learn

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What is Deep learning?

● In part, it is rebranding of old technology.● First model of Artificial Neural Networks was proposed in 1943 (!).● The analogy to the human brain has been greatly exaggerated.

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Deep learning

● Training deep networks in practice was only made possible by recent advances.

● Many state of the art breakthroughs in recent years (2012+), powered by the same underlying model.

● Some tasks can now be performed at human accuracy (or even above!).

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Deep learning

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Raw inputManual feature

extractionAlgorithm Output

● These algorithms are very good at difficult tasks, like pattern recognition.● They generalize when given sufficient training examples.● They learn representations of the data.

Raw inputDeep

Learning algorithm

Output

Traditional ML

Deep Learning

Deep learning is already in your life

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Deep learning is good with images

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Deep learning games and art?

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Deep learning on the road

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What follows

There is A LOT to learn! Many active research areas and infinite applications.

We are living a revolution.

● Rapid progress, need to keep up ;)● Entry barrier for using these technologies is

lower than ever.● Processing power keeps increasing, models

keep getting better.● What is the limit?

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Stay tuned. Thank you :)@tryolabs

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