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Teaching machines about human learning
- 1. Teaching machines about human learning Personalizing skill development at Degreed Marissa Saunders Data Scientist | Degreed marissa@degreed.com 1
- 2. Hello
- 3. Data is the defensible barrier, not algorithms 3 Andrew Ng
- 5. Agenda 5 What does Degreed do? How we use data science to understand our users better?
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- 10. Machine Learning task: Predict which category a piece of content belongs to using the title and description of the content. Choosing categories 10 Generating training data Training model Evaluating model
- 11. Popular tags
- 12. Choosing categories 12 Criteria • Based on our user’s interests & completions • Separability • Concept size • Data density • Context independent • Linked to key words and phrases Method • Manually derived, data driven • Iterate based on model results • Eventually, clustering
- 13. Generating training data 13 Define the category schema Derive training data from tags on content
- 14. Quantifying Certainty 14 Distribution of counts
- 15. Evaluating training data 15 Separability Categories per item Data density Items per category Coverage How much content is in the training data? 218559 / 436290 items Quality For a subsample, how good are the assigned categories? 78% precision
- 16. The model 16 So | you | want |to | be | a | data | scientist | so you| …. data scientist So you want to be a data scientist …
- 17. The model 17 So | So you | So you want | …. data scientist Semantic map Word embeddings So you want to be a data scientist …
- 18. The model 18 So | So you | So you want | …. data scientist 73 categories Hidden layer Word embeddings Category embeddings So you want to be a data scientist …
- 19. The model 19 So | So you | So you want | …. data scientist | scien | cient|ienti|… 73 categories Hidden layer Word embeddings Category embeddings So you want to be a data scientist …
- 20. How does it do? 20 Overall model precision Test set: 50K items P@1: 48.9% Most popular category: 4.8% Breakdown by category
- 21. Where are we making mistakes? 21 Which categories are content wrongly categorized to? Data Science False Negative categories Business management False Negative categories
- 22. The model 22 So | So you | So you want | …. data scientist | scien | cient|ienti|… Hidden layers So you want to be a data scientist … Word embeddings tSNE Sentence vectors
- 23. sales customer service & support education industry science science lifestyle excel emotional intelligence healthcare health & fitness news & politics marketing_ advertising data science productivity communication & conversation web development digital marketing
- 24. How do we use this?
- 25. Results from applying to my history: 25 What I’m Learning About Right Now Data Science Learning Software Engineering Programming Languages Business Intelligence Leadership Cognition Agile Management Human Resources Business Culture
- 26. Using this to personalize learning 26 Data Science Programming Languages Software Engineering Building Relationships Leadership
- 27. Key takeaways 27 Bootstrapping training data Neural networks for text categorization Metrics that guide improvement Human-machine interplay
- 28. Questions 28