1. Machine Learning & Data Science
Sylvia Unwin
Faculty, Program Chair
Assistant Dean, iBIT

2. Machine Learning Attended TDWI in Oct 2017
Focus on Machine Learning, Data Science, Python, AI
Started with a catchy opening speech – “BS-Free AI For Business”
Top 5 BS List
TDWI Transforming Data with Intelligence
Old: The Data Warehouse Institute

3. AI What’s the BS? AI is first
According to the speaker, doesn’t solve a necessary real-world problem
Startups (investments) in scaling AI
Doesn’t show ROI without promise of more, perfect and better data

4. Avoid Big data problem will only provide a small data solution
Thinking more data will solve the problem (if perfect data, will work)
Not defining what is the problem?
Be specific (reduce waste by 10x)
Know who owns the data
Avoid scaling too quickly
Reduce waste by 10%, increase surplus by 10x, increase retention by 10%, increase graduation by 5%, impact real-world somehow

5. Avoid No Black boxes Inaction
Requires trust, then must have transparency
No technical explanations (too many acronyms), no invented scores
Inaction
“nothing will happen, if no action is taken”

6. Why AI Be aware of your focus Understand the data (common theme)
Scalability
Take action

7. Machine Learning using Python
Continuously improving models
Cost reduction
Classification of space data
Definitions of various models
Regression Pattern Recognizer
Classification
Clustering
Definition of Terms and review of concepts

8. Classification Supervised Unsupervised Discrete vs Continuous values
Trained with data, fully labeled, user involved with training
Unsupervised
No training data, groupings of similar attributes (characteristics), computer uses techniques such as clustering
Discrete vs Continuous values

9. Understand Which Algorithm to Use
Categorical
(Discreet)
Continuous
Supervised
Classification
Regression
Unsupervised
Clustering

10. Algorithms Logistic Regression Neural Networks Decision Trees
Simple, large scale, can be parallelized
Neural Networks
Unstructured data, no limit to complexity, good on large datasets
Decision Trees
Easy to interpret, fast prediction, rules based

11. Evaluate Model All data available Run through the model Output
Split to training and testing data
Run through the model
Output
Train model, measure performance

12. Examples Predict Price of houses Book recommendation
Petal vs Sepal of Iris
Walmart – beer & diapers
To use in the classroom

13. Other Confusion Matrix Train/Test More data or more model
Solve binary problem, how wrong
Train/Test
Cross validation; split data into slices, then have a different assessment and average it out
More data or more model
Build a learning curve
Confusion Matrix

14. Jupyter Navigator
Jupyter Notebook
Examples in Python
Not enough time

15. Data Visualization Know your audience Mechanism for feedback
How to direct the focus
Charts, images
Develop a sense of storytelling
Know your data
Relationship to user
Be creative

16. Data Science May be a data artist Storytelling
Problem & data = acceptable solution
Storytelling
Make the analytics tell a more focused story
Don’t undervalue hands-on experience
Target something useful
Analytics is AI

17. Robotics & AI Validated topics introduced
Statistics
Data Analytic techniques
Data visualization
Not all science, there is some art
Python programming
“AI is first”
Data Analytic techniques
Classification, Linear Regression, Decision Trees