Machine learning - session 4
- 1. Machine Learning Lunch & Learn - Session 4 Luis Borbon 11/07/2017
- 2. Table of contents 1. Recap 2. Generalization in Machine Learning 3. Overfitting and Underfitting 4. Algorithms by Similarity 5. Real Application 6. People to follow
- 3. Recap
- 4. Recap ● Training, validation and test data sets. ● Learning Style ○ Supervised ○ Unsupervised ○ Semi-Supervised Learning. ● Similarity ○ Regression Algorithms ○ Instance-based Algorithms ○ Regularization Algorithms ○ Decision Tree Algorithms
- 6. Recap Decision trees Possible applications in PlantMiner: For a searcher: Based on previous quotes, identify an item that usually is being hired along other. ● Suggest the item. ● Offer a discount to add the suggested item. For a supplier: Identify suppliers that would crunch on the next subscription renewal.
- 7. Generalization in Machine Learning
- 8. Induction and deduction Induction refers to learning general concepts from specific examples which is exactly the problem that supervised machine learning problems aim to solve. This is different from deduction that is the other way around and seeks to learn specific concepts from general rules.
- 9. Induction and deduction The goal of a good machine learning model is to generalize well from the training data to any data from the problem domain. This allows us to make predictions in the future on data the model has never seen.
- 11. Overfitting In machine learning, one of the most common tasks is to fit a "model" to a set of training data, so as to be able to make reliable predictions on general untrained data. In overfitting, a statistical model describes random error or noise instead of the underlying relationship. The green line represents an overfitted model and the black line represents a regularised model. While the green line best follows the training data, it is too dependent on it and it is likely to have a higher error rate on new unseen data, compared to the black line.
- 12. Overfitting A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data. Noisy (roughly linear) data is fitted to both linear and polynomial functions. Although the polynomial function is a perfect fit, the linear version can be expected to generalize better. In other words, if the two functions were used to extrapolate the data beyond the fit data, the linear function would make better predictions.
- 13. Overfitting Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. Overfitting/overtraining in supervised learning (e.g., neural network). Training error is shown in blue, validation error in red, both as a function of the number of training cycles. If the validation error increases(positive slope) while the training error steadily decreases(negative slope) then a situation of overfitting may have occurred. The best predictive and fitted model would be where the validation error has its global minimum.
- 14. Underfitting Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. It occurs when the model or algorithm does not fit the data enough. Underfitting occurs if the model or algorithm shows low variance but high bias (to contrast the opposite, overfitting from high variance and low bias). It is often a result of an excessively simple model. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model would have poor predictive performance.
- 15. Underfitting There are two important techniques that you can use when evaluating machine learning algorithms to limit overfitting: ● Use a resampling technique to estimate model accuracy. ● Hold back a validation dataset.
- 16. Underfitting Resampling The most popular resampling technique is k-fold cross validation. It allows you to train and test your model k-times on different subsets of training data and build up an estimate of the performance of a machine learning model on unseen data. Validation dataset A validation dataset is simply a subset of your training data that you hold back from your machine learning algorithms until the very end of your project. After you have selected and tuned your machine learning algorithms on your training dataset you can evaluate the learned models on the validation dataset to get a final objective idea of how the models might perform on unseen data.
- 17. Algorithms by Similarity (cont…)
- 18. Bayesian Algorithms Bayesian methods are those that explicitly apply Bayes’ Theorem for problems such as classification and regression. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis. Document classification, based on word frequencies. e.g. SPAM.
- 19. Bayesian Algorithms The most popular Bayesian algorithms are: ● Naive Bayes ● Gaussian Naive Bayes ● Multinomial Naive Bayes ● Averaged One-Dependence Estimators (AODE) ● Bayesian Belief Network (BBN) ● Bayesian Network (BN)
- 20. Real Application
- 21. DoseMe.com.au Bayesian dosing uses patient data and laboratory results to estimate a patient's ability to absorb, process, and clear a drug from their system. Using a published population model, DoseMe's algorithms adjusts the pharmacokinetic and/or pharmacodynamic parameters so that a patient-specific, individualised drug model is built. This individual model is then used to provide a patient-specific dosing recommendation to reach a therapeutic target.
- 22. People to Follow
- 23. Fei-Fei Li Fei-Fei Li, who publishes under the name Li Fei-Fei, is an Associate Professor of Computer Science at Stanford University. She is the director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. ● Born: 1976, Beijing, China ● Spouse: Silvio Savarese ● Education: California Institute of Technology (2005) ● Residence: United States of America ● Books: Computer Vision: From 3D Reconstruction to Visual Recognition, more ● Doctoral advisors: Pietro Perona, Christof Koch ● http://vision.stanford.edu/feifeili/ ● @drfeifei
- 24. Andrej Karpathy Director of AI at Tesla, currently focused on perception for the Autopilot. Previously, I was a Research Scientist at OpenAI working on Deep Learning in Computer Vision, Generative Modeling and Reinforcement Learning. PhD from Stanford, where I worked with Fei-Fei Li on Convolutional/Recurrent Neural Network architectures and their applications in Computer Vision, Natural Language Processing and their intersection. ● http://cs.stanford.edu/people/karpathy/ ● @karpathy
- 25. OpenAI Gym Founded: December 11, 2015 Founders: Elon Musk, Sam Altman, and others Type: 501(c)(3) Nonprofit organization Location: San Francisco, California, USA Products: OpenAI Gym Mission: Friendly artificial intelligence ● https://www.openai.com/ ● @OpenAI