Lecture 1:
Theory
Lecture 2: Code Walk-Through
Hands-on Session: Small Group
Fundamentals in in Neural Networks (Regression)
Linear Regression, Training/Testing
Hands-on work
Hands-on work
Hands-on work
Hands-on work
Hands-on work
Logistic Regression
Polynomial Regression, Overfitting, and Tuning
Week 1
Week 2
Week 3
Week 4
Week 5
Intro to Data Science & Exploratory Data Analysis (EDA)
Polynomial Regression, Tuning a Model
EDA, Train/Test Split, Linear Regression
Logistic Regression & Multiple Logistic Regression
Introduction of Tensorflow Keras and Neural Networks
Intro to Python & Basic Programming
Course Structure
Closing
Ceremony
Project: Baseline Model
Presentations
Project work
Project: Advanced Model (Upgrade from Baseline)
Week 6
Week 7
Week 8
Week 9
Week 10
Convolutional Neural Networks (CNNs)
Tools for Improving CNNs: Regularization and Transfer Learning
Image Classification with CNNs
Ethics in AI
Advanced Topics in Image Classification: Using VGG16
Project work
Project work
Tuning Neural Networks (Classification)
Project: Start Projects with EDA!
Tuning NNs, Using NNs for classification, Validation Sets
Course Structure
CAPSTONE PROJECT
Chat Waterfall: What is AI?
What is AI?
Vision
Composition
Drug discovery
Diagnosis
What is AI?
Vision
Composition
Drug discovery
Diagnosis
To make the best AI, you first need to do good data science. Ask questions like…
Let’s see an example of how knowing your data leads to better predictions…
Abraham Wald and the Allied Forces Statistical Research Group
Image courtesy of Professor Joseph Blitzstein (i.e. the best stats prof in the country). See his “Harvard Thinks Big” talk on this problem: https://youtu.be/dzFf3r1yph8
Chat Waterfall: Where would you put extra armor?
a) Nose (front)
b) Main wings
c) Main body
d) Engines
e) Tail (back)
Image courtesy of Professor Joseph Blitzstein (i.e. the best stats prof in the country). See his “Harvard Thinks Big” talk on this problem: https://youtu.be/dzFf3r1yph8
Where would you put extra armor?
a) Nose (front)
b) Main wings
c) Main body
d) Engines
e) Tail (back)
Wald’s Answer
Image courtesy of Professor Joseph Blitzstein (i.e. the best stats prof in the country). See his “Harvard Thinks Big” talk on this problem: https://youtu.be/dzFf3r1yph8
These are the planes that came back.
So these dots are places where planes can take hits and still make it back.
Image courtesy of Professor Joseph Blitzstein (i.e. the best stats prof in the country). See his “Harvard Thinks Big” talk on this problem: https://youtu.be/dzFf3r1yph8
The planes not included in our sample – the ones that went down – may have looked like this.
The Plane Problem:
Sample Bias
Where does sample bias come up in modern AI problems?
Models built from existing stock data ignore firms that left the market (& the factors that made them go broke).
Predicting Stock Prices
Self-Driving Cars
Perform worse on roads that look very different than the ones they’ve seen before
Facial Recognition
Shows bias if trained on datasets with predominantly white faces
Exploratory Data Analysis (EDA)
EDA is critical to all data projects (and will be needed for your projects in a few weeks!)
EDA helps us:
Why Exploratory Data Analysis (EDA)?
Categorical Variables
Rapid Fire Chat: List some examples!
Quantitative Variables
Rapid Fire Chat: List some examples!
Pop Quiz
Chat Waterfall: Are zip codes categorical or quantitative data? Why?
EDA for Categorical Data
One Variable Visual
Barplot
Two Variable Analysis Two-Way Tables + Barplots
Summary Stat Proportions
Percents of sample:
EDA for Quantitative Data
One Variable Visual
Histogram or Boxplot
Two Variable Analysis Scatterplot + Correlation
Summary Stat 5-number summary
Heights of people
r = 0.76
Correlation