Artificial Intelligence and Big Data Science [Q3, 2019]

Introduction

• From Big Data to AI via Data Science
  • Data-driven Decision Making for Data-driven Organizations
  • What are Big Data, Data Science and AI?
  • How do Big Data, Data Science and AI support Data-driven Decision Making?
  • Who is using Big Data, Data Science and AI?

Diving into AI

• solving problems the AI way [drawings]
  • coding vs. machine learning/AI
  • Gradient Descent as a general procedure
• supervised machine learning
  • Predicting the price of diamonds (a prototypical regression problem) with linear regression [drawing]
    • finding the regression line using gradient descent [drawing]
    • what if the relationship is not linear? Polinomial fitting vs. data preparation (a.k.a. feature enginnering) [drawing]
  • Supporting Human Resources in hiring fresh graduates (a prototypical classification problem) based on Logistic Regression [drawing]
    • finding the regression line using gradient descent [drawings]
  • A single neuron in a neural network is “just” a binary classifier
    • when a line is enough
    • when a line is still enough after data preparation
  • A neural network can solve complex classification problems
    • a sigle layer of neurons hardly works (even if in theory enough neurons do)
    • multiple layers (deep learning) easily solves the problem
• unsupervised machine learning
  • Placing shops given distribution of buyers with desired segment solution using k-means [video]

Final discussion

• beware confirmation bias
• Ethics and legal implications

Do you want to learn more?

Check-out “Data Science for Business Innovation” on Coursera

It’s free!