Think Like a Machine

Course Outline

Course Materials

• Data Science for Business by Foster Provost and Tom Fawcett
• Andreesen Horowitz AI Playbook (http://aiplaybook.a16z.com/)
• The Simple Economics of Machine Intelligence (HBR article - https://hbr.org/2016/11/the-simple-economics-of-machine-intelligence)
• Data sets
• Blogs and articles
• Download Anaconda and instructions for that - WRITE UP INSTRUCTIONS

Session 1

• Machine learning and AI in the press -- how to separate the hype from the reality?
• Examples of companies using machine learning to great benefit -- we've all heard of the recommender systems at Netflix and Amazon, but what else? Look at Information Week's Elite 2016 companies like Capital One and Andreesen Horowtiz examples (http://www.informationweek.com/elite100-2016.asp, http://aiplaybook.a16z.com/).
• The economics of machine learning (a16z and HBS article)
• The basic tasks of machine learning (Provost and Fawcett pp. 20-23)
• EXCERCISE: What should the data look like for each of these tasks?

Session 2

• What makes machine learning different from other approaches to learning from experience?
  • Bottom up not top down (not rules but experience)
  • Lots of data but not necessarity big data
  • Data is just a table of numbers
  • Experience, tasks, models, cost functions, and optimization algorithms
  • Machine learning tasks are giant numerical optimization problems
  • Machine learning system = Model + Cost Function + Cost Optimization Algorithm
  • Example of how the results change when the knobs are moved
  • Key operational questions for machine learning -- the switches and knobs on your control panel.
  • The promise and limits of machine learning.

Session 3

• Linear regression -- motivating examples
  • Wine data
  • Housing prices
  • ...
• Visualize the data (whenever possible)
• Define the inputs
• Define the outputs
• Define the model
  • How the model translates into a matrix
  • All you need to know about matrix algebra -- the multiplication rule
  • Matrix notation helps you parse expressions such as $J(\theta) = \frac{1}{2m} \sum_{i=1}^{m} (h_{\theta}x^{(i)}) - y^{(i)})^2$
• Define the parameters of the model
• Define the cost of getting it wrong
• Pick a method for miminimizing the cost of getting it wrong and implement it
• Check to see if your results make sense

Session 4

• Multi-variable and non-linear regression

Session 5

• Logistic regression

Session 6

• Neural networks for classification
• Support Vector Machines for classification

Session 7

• Evaluating and fine tuning machine learning models
  • Bias and variance
  • Precision and Recall
  • Learning curves
  • Error analysis
  • Ceiling analysis

Session 8

• Measuring similarity using K-means

Session 9

• Principal Component Analysis and dimensionality reduction

Session 10

• Anomaly detection

Session 11

• Recommender systems

Session 12

• Representing and learning from text

Session 13

• Techniques for handling big data
  • Stochastic gradient descent
  • Mini-batch gradient descent
  • Splitting up data for parallel processing