Machine learning - session 5
- 1. Machine Learning Lunch & Learn - Session 5 Luis Borbon 13/07/2017
- 2. Table of contents 1. Recap 2. Support Vector Machine 3. Real Application 4. People to follow
- 3. Recap
- 4. 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.
- 6. Algorithms by Similarity (cont…)
- 7. Support Vector Machine (SVM) It is a classification method. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.
- 8. Support Vector Machine We have a population composed of 50%-50% Males and Females. Using a sample of this population, you want to create some set of rules which will guide us the gender class for rest of the population.
- 9. Support Vector Machine Find the boundary that separates classes by as wide a margin as possible. When the two classes can't be clearly separated, the algorithms find the best boundary they can. Because it makes this linear approximation, it is able to run fairly quickly. Where it really shines is with feature-intense data, like text or genomic. In these cases SVMs are able to separate classes more quickly and with less overfitting than most other algorithms, in addition to requiring only a modest amount of memory.
- 11. Support Vector Machine Not a clear frontier?
- 12. Support Vector Machine Data that is not linearly separable? http://efavdb.com/svm-classification/
- 13. Support Vector Machine ● Pros: ○ It works really well with clear margin of separation ○ It is effective in high dimensional spaces. ○ It is effective in cases where number of dimensions is greater than the number of samples. ○ It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. ● Cons: ○ It doesn’t perform well, when we have large data set because the required training time is higher ○ It also doesn’t perform very well, when the data set has more noise i.e. target classes are overlapping ○ SVM doesn’t directly provide probability estimates, these are calculated using an expensive five-fold cross-validation. It is related SVC method of Python scikit-learn library.
- 14. Real Application
- 15. PopGun.ai Popgun is using Deep Learning to create an exciting new musical experience. We are building an AI that can play music with you, just like a professional musician. We think this AI will become an essential learning and creative tool for musicians worldwide.
- 16. People to Follow
- 17. Brand within the AI social media debate
- 18. Jensen Huang Jensen Huang is a Taiwan-born American entrepreneur and businessman. He co-founded the graphics-processor company Nvidia and serves as its president and CEO. Huang graduated from Oregon State University before moving to California. ● GTC 2017 https://youtu.be/WLq9zv3k5n0