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"AI is the new electricity" 3

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Machine Learning 101 An introduction for developers 5

Agenda 1. Python & its benefits 2. Types of Machine Learning problems 3. Steps needed to solve them 4. How two classification algorithms work a. K-Nearest Neighbors b. Support Vector Machines 5. Evaluating an algorithm a. Overfitting 6. Demo 7. What follows 6

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 7

Data Science “Swiss army knife” Data preparation / exploration / visualization ● Pandas ● Matplotlib ● Seaborn Modeling / Machine Learning ● Scikit-learn ● Pylearn2 Text focused ● Gensim ● nltk Deep learning ● Keras ● TensorFlow, Theano 8

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) 9 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 gathering 2. Data processing & feature engineering 3. Algorithm & training 4. Applying model to make predictions (evaluate, improve) 10

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). 11

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? 12

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 ○ Number of emojis 13 Buy ch34p drugs from the ph4rm4cy now :) :) :) :) (1, 2, English, 4) Feature engineering

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) 14

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. 15

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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. 17

18 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. 19

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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). 21

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. 22

Support Vector Machines: Kernel Trick 23

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 24

Evaluating a spam classifier: example 25 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! 26

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! 27

Demo: scikit-learn 28

What follows There is A LOT to learn! Many active research areas. ● Dealing with natural language ○ Language understanding ○ Question answering ○ Automatic summarization ● Feature/Representation learning ○ Embeddings (ie. word2vec) ● Neural networks ○ Deep learning ○ Cool applications to signals (image, videos, sounds) ○ Generation of image captions, etc. 29

Stay tuned. Thank you :) 30