Machine Learning Introduction
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The document discusses the basics of machine learning, emphasizing the importance of data management, feature engineering, and model training. It highlights that proper data preprocessing and thoughtful training processes are crucial to avoiding overfitting and bias in machine learning algorithms. The speaker, Demetris Trihinas, stresses the need for human oversight in generating insights from ML models.
Machine Learning Introduction
- 1. 5/22/19 1Demetris Trihinas trihinas.d@unic.ac.cy 1ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Machine Learning Panel Opening Comments Demetris Trihinas Department of Computer Science AILab @ University of Nicosia trihinas.d@unic.ac.cy
- 2. 5/22/19 2Demetris Trihinas trihinas.d@unic.ac.cy 2ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Full-Time Faculty Member University of Nicosia “Designing and developing scalable and self-adaptive tools for data management, exploration and visualization” @dtrihinas http://dtrihinas.info https://ailab.unic.ac.cy/https://www.slideshare.net/DemetrisTrihinas
- 3. 5/22/19 3Demetris Trihinas trihinas.d@unic.ac.cy 3ACCA ML Panel| Nicosia, May 2019 Department of Computer Science What is NOT Machine Learning • Any question you can ask and get an –immediate and concrete– answer (e.g., database, spreadsheet). • How many sofas models are currently in stock? • How many sofas did we sell in Germany last month? • Which of our customers bought a sofa worth more than 500 euros this quarter?
- 4. 5/22/19 4Demetris Trihinas trihinas.d@unic.ac.cy 4ACCA ML Panel| Nicosia, May 2019 Department of Computer Science The Machine Learning Process Data and Labels Feature Engineering ML Algorithm Bike Car Bike < 2, 170, 35, 169, 51, 38, …> < 2, 119, 28, 210, 52, 02, …> Car <4, 13, 157, 90, 178, 145, …> <4, 12, 170, 82, 193, 145, …> wheels Training Testing Data Feature Engineering Inferencing Statistical Model <4, 18, 200, 64, 170, 141, …> It’s a… Car Finding ”patterns” from features ! = #(%, ')
- 5. 5/22/19 5Demetris Trihinas trihinas.d@unic.ac.cy 5ACCA ML Panel| Nicosia, May 2019 Department of Computer Science ML Modeling is an Approximation... Feature Engineering ML Algorithm Training Testing Data Feature Engineering Statistical Model <4, 18, 200, 64, 170, 141, …> <3, 22, 23, 31, 101, 205, …> Data and Labels Inferencing It’s a Car I’m 0.88 sure It’s a Car I’m 0.71 sure
- 6. 5/22/19 6Demetris Trihinas trihinas.d@unic.ac.cy 6ACCA ML Panel| Nicosia, May 2019 Department of Computer Science It’s Also Called “Learning” Because… Feature Engineering ML Algorithm Training Testing Data Feature Engineering Statistical Model <4, 18, 200, 64, 170, 141, …> It’s a… Car, 0.88 <3, 22, 23, 31, 101, 205, …> It’s a… Car, 0.71 <3, 22, 23, 31, 101, 205, …> It’s a… tricycle, 0.93 Data and Labels Evaluate and RetrainInferencing
- 7. 5/22/19 7Demetris Trihinas trihinas.d@unic.ac.cy 7ACCA ML Panel| Nicosia, May 2019 Department of Computer Science How Do Robots See Us
- 8. 5/22/19 8Demetris Trihinas trihinas.d@unic.ac.cy 8ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Training is of Utmost Importance • More training data is good but too much leads to overfitting (irrelevant details are modeled). • Algorithms are not racist, do not hold prejudice or apply stereotypes… yes, but what happened to Amazon? Memorizing the answers is NOT Learning
- 9. 5/22/19 9Demetris Trihinas trihinas.d@unic.ac.cy 9ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Beware of the “Trainer”… https://www.businessinsider.com/amazon-built-ai-to-hire-people-discriminated-against-women-2018-10
- 10. 5/22/19 10Demetris Trihinas trihinas.d@unic.ac.cy 10ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Data Preprocessing • Preprocessing significantly increases ML performance and result quality. • Is 1% more error tolerable if computation “promises” to run for 10min instead of 5 hours? • Data Reduction –remove insignificant model dimensions. • Data Cleaning –remove incomplete and “dirty” data. Do not “influence” result
- 11. 5/22/19 11Demetris Trihinas trihinas.d@unic.ac.cy 11ACCA ML Panel| Nicosia, May 2019 Department of Computer Science What is YOUR Role? • Ask good questions – a model is based on a hypothesis. • Provide training data – BEWARE training can lead to bias. • Assess the quality of results – retrain let the model “learn”. • TEST, TEST and TEST again. The Ability Matrix Insights are generated by humans not machines!
- 12. 5/22/19 12Demetris Trihinas trihinas.d@unic.ac.cy 12ACCA ML Panel| Nicosia, May 2019 Department of Computer Science Questions? Demetris Trihinas Department of Computer Science AILab @ University of Nicosia trihinas.d@unic.ac.cy