7 ms·
An introduction to Machine Learning
- aerioux 11y agothat was a really good introduction :) sort of like an executive summary - all the "why we care" and some of the words you might want to look at to actually learn the details
- yelnatz 11y agoPretty good summary of what you learn in your first machine learning class in college.
- remriel 11y agoThank you.
- antoineaugusti 11y agoPlease note that I'm not the author of the presentation. Made by Quentin de Laroussilhe http://underflow.fr http://underflow.fr I had to make a copy to my Google account to keep the slides.
- max_ 11y agoThanx for sharing this!!
- Dowwie 11y agois there a corresponding video where the slides are presented?
- antoineaugusti 11y agoNope, sorry. This presentation was given by Quentin de Laroussilhe in Paris at EPITA recently.
- aabajian 11y agoThis really is a fantastic presentation for newcomers to the field. When I was taking these classes I found it difficult to keep all of the available algorithms organized in my mind. Here's an outline of his presentation: Overview (5 slides) General Concepts (9 slides) K nearest Neighbor (6 slides) Decision trees (6 slides) K means (4 slides) Gradient descent (2 slides) Linear regression (9 slides) Perceptron (6 slides) Principal component analysis (6 slides) Support vector machine (6 slides) Bias and variance (4 slides) Neural networks (6 slides) Deep learning (15 slides) I especially like the nonlinear SVM example on slides 57 and 58. It provides a visual of projecting data into a higher dimensional space.
- fizixer 11y agoThanks for the great slides. Some questions: I'm a bit confused trying to understand "error function" vs "loss function" (going from Linear regression to perceptron). Coming from a numerical background: - Is the term 'error function' used as a special function (like sin, cosine, etc) or is it a generalized term ? - If it's a special function (the one that looks like MSE [2]), then it's confusing because 'error function' as a fixed/special function is erf [1] also known as Gauss error function (and looks completely different). - Are we using the term 'loss function' as a generalized term? whose special case is 'error function'? e.g., in linear regression loss function is 'error function' (MSE like function) but in perceptron, loss function is max(0, -xy)? - Using final output of perceptron for error function makes it a "hard problem" agreed. But what about using just the function from linear regression (the MSE-like one) instead of a using a brand new function max(0, -xy). (It's not very intuitive to reason what's so special about max(0, -xy)). - Also wondering why do we not use RMSE instead of MSE in linear regression. (But it might have a known explanation in statistics texts, so somewhat off-topic). [1] https://en.wikipedia.org/wiki/Error_function https://en.wikipedia.org/wiki/Error_function [2] https://en.wikipedia.org/wiki/Mean_squared_error https://en.wikipedia.org/wiki/Mean_squared_error
- toxik 11y agoThe reason they call it error function in perceptron learning is that it relates to how the perceptron is taught a correction for an error. Loss functions are more general and usually the word people use when they're talking about optimization problem.s
- rafaquintanilha 11y agoWorth to mention that a Statistical Learning Stanford course [1] just started and according to the lecturers there is a lot of overlap in both areas. [1] https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
- lectrick 11y agoIs there an online course for this I could take?
- kendallpark 11y agoI second this question. I couldn't find one on coursera or academic earth.
- reverius42 11y agoI liked the UW Coursera class that gave a broad overview of these topics with some applications: https://www.coursera.org/learn/ml-foundations https://www.coursera.org/learn/ml-foundations It's part of a Machine Learning Specialization on Coursera (5 courses + a capstone project) which goes deeper on some areas after the foundations course: https://www.coursera.org/specializations/machine-learning https://www.coursera.org/specializations/machine-learning I am taking this specialization and I have learned a lot so far. The material seems like it's at exactly the right level of depth (balances giving a high level overview of the field, with enough depth in specific areas to understand how things work and be able to apply them). Disclaimer: I work at Dato, and the CEO of Dato is also one of the instructors of this course.
- tgokh 11y agoThis "Statistical Learning" course has just started on Stanford's online platform last week: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
- synotic 11y agoAndrew Ng's popular Machine Learning course goes over most of the topics in the slides: https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning Linear and logistic regression, gradient descent, clustering, support vector machines, bias and variance (one of the slides was taken from the course), neural networks, etc...
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- kendallpark 11y agoYes, thank you. I'm hoping to build an ANN this summer and don't have the luxury of taking an actual class. Does anyone have any other resources?
- bl4ckdu5t 11y agoYou should look up marI/O on YouTube it may be a good starting point for you
- kendallpark 11y agoThanks! I'll checkout it out.
- compactmani 11y agoIf you are just starting out with applied machine learning I would focus heavily on understanding bias and variance as it will really help you succeed. It's I think what (largely) separates the sklearn kiddies from the pros.
- johnmarinelli 11y agowhat's wrong with sklearn?
- ivan_ah 11y agoNothing wrong, quite the opposite scikit-learn is awesome. I think the comment was a word play on "script kiddie" (ppl w/o real "security chops" but who know enough to run an exploit "script" of some sort).
- fnl 11y agoNobody concerned about plagiarism here? I am pretty sure I've seen a number of the slides and graphics elsewhere. Correct attributions however seem amiss.
- underflow 11y agoI did those slides for a talk at school at the very last minute and I did not expect it to be republished. I requested the edit rights on the document and I'll fix this asap.