Introduction to Data Science Lesson 1 Intro: Dr. Amitai Armon, Chief Data Scientist, Intel Advanced Analytics

Administrative Details Course lecturer: Prof. Tova Milo milo@cs.tau.ac.il Course teaching assistant: Slava Novgorodov slavanov@post.tau.ac.il Grade structure: 30% Exercises 70% Final Exam Course website: http://slavanov.com/teaching/ds1718b/

Course Topics This course will provide a practical introduction to machine-learning and big data Main topics of the classes: A brief introduction to Machine Learning & Artificial Intelligence Data Understanding and Data Preparation Feature Selection and Model Evaluation Supervised Modeling Unsupervised Modeling Deep Learning Introduction to Big Data Spark NoSQL databases Spark Streaming

Exercises There will be four exercises during the course The last exercise will be bigger Exercises will be in Python Submission is in pairs See the course website: http://slavanov.com/teaching/ds1718b/

Administrative Details Questions?

A little bit about us: Machine Learning & Artificial Intelligence at Intel We Enable the ML & AI Market We Use ML & AI to Make Smart Products We Use AI to Upgrade Our Own Operations

Intel’s Advanced Analytics department A group of 120 Data-Scientists, Big Data Developers and Product Experts located in Israel Sales & Marketing Design Radically improving operations manufacturing Effective validation Lower cost, higher quality Increasing ROI and scale Health Analytics Building smart products & Enabling the AI Market Embedded AI Industrial AI Processors that learn Smart clinical trials IOT platform for factories

Machine Learning & Artificial Intelligence ARE Everywhere… Handwriting recognition Automatic translation Recommendations of products/websites Credit-card fraud detection Speech recognition Algo-trading Personal assistant applications Autonomous cars ….

Answering Visual Questions Kan et al., 2015

Dialogue (“Turing Test”) Google chatbot, 2015

What is Artificial Intelligence? a machine mimics "cognitive" functions that humans associate with other human minds [Wikipedia] As machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, leading to the saying "AI is whatever hasn't been done yet”

What is Machine Learning? A branch of artificial intelligence, concerns the construction and study of systems that can learn from data. [Wikipedia] Alternative definition: Constructing systems that use data to improve in achieving a goal X1 X2 More complex input data and tasks often require more sophisticated models Classification is just one task type, other examples are regression and recommendations

Machine Learning modeling techniques Machine Learning: Using DATA to learn (define) how to achieve a goal Machine Learning Neural Networks X2 X1 Deep Learning There are dozens of Machine-Learning modeling methods

“A Brief History of Machine-Learning” Graph created by Eren Golge, published in Kdnuggets, Oct. 2014

Typical Machine Learning Tasks Supervised Learning Learning from labeled examples (for which the answer is known) Unsupervised Learning Learning from unlabeled examples (for which the answer is unknown) Semi-supervised Learning Learning from both labeled and unlabeled examples Active Learning Learning while interactively querying for labels of examples Reinforcement Learning Learning by trial and feedback, like “child learning”

Illustration: Supervised learning Step 1: Training In Data Center – Over Hours/Days/Weeks Step 2: Inference End point or Data Center - Instantaneous Person Lots of labeled input data New input from camera and sensors Create model Trained Model Trained neural network model 97% person 2% traffic light Output: Trained Model Output: Classification

Supervised Learning Features X1,…Xn Label X1 X2 X3 … Xn-2 Xn-1 Xn Y . x1,m-1 x2,m-1 x3,m-1 xn-2,m-1 xn-1,m-1 xn,m-1 ym-1 x1,m x2,m x3,m xn-2,m xn-1,m xn,m ym Samples 1,…,m Uses a set of labeled examples with known answer (“training set”) Success is evaluated on a separate set of examples (“test set”). Various success criteria may be considered: For classification: Accuracy, Recall, Precision… For regression: MSE, RMSE,…

“Child Learning” Action Reaction Lesson Touching hot stove aching hand Do not touch again Playing with toys Fun Continue playing Running in to the road Screaming parent Don’t run to roads Running in the house Run in the house Eating chocolate Search for chocolate Eating too much chocolate Stomach ache Don’t eat too much Saying bla bla No Reaction Try variations Saying daddy Overexcited parents Do that again

Evaluating What Has Been Learned Test set 2. Cross Validation Confusion Matrix Classified As Red Blue 1 7 5 Actual

Regression Learning Example

Overfitting and Underfitting Overfitting: The model learns the training set too well – it over fits the training set such that it cannot generalize to new instances. Underfitting: the model is too simple, both training and test errors are large

CRISP-DM Data Mining Methodology CRISP-DM breaks the process of data mining into six major phases Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment The sequence of the phases is not strict and moving back and forth between different phases may be required

Course Topics Overview A brief introduction to Machine Learning & Artificial Intelligence Data Understanding and Data Preparation Feature Selection and Model Evaluation Supervised Modeling Unsupervised Modeling Deep Learning Introduction to Big Data Spark NoSQL databases Spark Streaming

Brief Introduction to ML & AI Questions?