1. Introduction to Data Science Lecture 7 Machine Learning Overview
CS 194 Spring 2014
Michael Franklin
Dan Bruckner, Evan Sparks,
Shivaram Venkataraman

2. What is it?
“Machine learning systems automatically learn programs from data”
P. Domingos, CACM 10/12

3. Some Examples Classification Regression
Learned attribute is categorical (spam vs. ham)
Input: vectors of “feature values” (disc. or cont.)
Output: a single discrete value (a.k.a. the “class”)
Regression
Learned attribute is numeric
Fiting a curve to data
can then use that curve to predict outcomes

4. Classification Example
Learn a function that predicts given weather if someone will play golf?
From Bill Howe’s Coursera Class: Introduction to Data Science

5. Three Components of ML Algorithms
Representation
Language for the classifier
What the input looks like
Evaluation (scoring function)
How to tell good models from bad ones
Optimization
How to search among the possible models to find the highest-scoring one
P. Domingos, “A Few Useful Things to Know About Machine Learning”, CACM Oct 2012.

6. Some Examples of Them
P. Domingos, “A Few Useful Things to Know About Machine Learning”, CACM Oct 2012.

7. Terminology Supervised Learning Unsupervised Learning
Given examples of inputs and outputs (i.e., labeled data)
Learn the relationship between them
Unsupervised Learning
Inputs but no outputs (unlabeled data)
Learn the latent labels
e.g., clustering, dimension reduction
You get to do both in HW 2 (see rest of today’s reading ch for K-means)

8. Supervised Learning
Cat
Dog
???

9. Generalization is the Goal
Pick a subset of your data as the training set.
Train your model on that
Then, test it using the held back data (i.e., the test set)
Most important rule: Don’t Test on your Training Data (easy to predict the ones you’ve already seen!)

10. Overfitting Low error on training data, but High error on test data
Example:
Your classifier is 100% accurate on training data but only 50% accurate on test data, when it could have been 75% accurate on each

11. Cross-Validation
Holding back data reduces amount of data available for training.
Alternative:
Randomly divide training data into multiple subsets
hold out each one while training on the rest
average the results for evaluation

12. Feature Engineering
Constructing features of the raw data on which to learn
After gathering, integrating and cleaning, this is the next step
when do we get to run our learning algorithm?
Often domain-specific
Requires trial and error

13. Unsupervised Learning
“Deep Learning” from Google’s Brain project

14. Plan for Rest of This Evening
We’ll focus on Supervised Learning
In particular, Shivaram will cover Linear Regression in some detail
Followed by an R-based Lab
Finally – some HW2 programming tips based on what we saw in HW1 (Dan)
Announcement:
Midterm: Thursday April 17 6pm; Kroeber rm 160