Deep learning architectures are built using multiple levels of non-linear aggregators, for instance neural nets with many hidden layers. In this introductory talk Will Stanton discusses the motivations and principles regarding learning algorithms for deep architectures. Bill provides a primer to neural networks, and deep Learning. He explains how Deep Learning gives some of the best-ever solutions to problems in computer vision, speech recognition, and natural language processing.
and also, why Google is Investing in deep learning.
Here is Prof. Hastie's recent talk from the H2O World conference. In this talk, professor Hastie takes us through Ensemble Learners like decision trees and random forests for classification problems.
Other excellent talks from the conference include the following.
- Michael Marks - Values and Art of Scale in Business
- Nachum Shacham of Paypal - R and ROI for Big Data
- Hassan Namarvar, ShareThis - Conversion Estimation in Display Advertising
- Ofer Mendelevitch, Hortonworks - Bayesian Networks with R and Hadoop,
- Sandy Ryza, Cloudera - MLlib and Apache Spark
- Josh Bloch, Lord of the APIs - A Brief, Opinionated History of the API
- Macro and Micro Trends in Big Data, Hadoop and Open Source
- Competitive Data Science Panel: Kaggle, KDD and data sports
- Practical Data Science Panel
The complete playlist can be found here.
Over a decade ago, Peter Flach of Bristol University wrote a paper on the topic of "On the state of the art in machine learning: A personal review" in which he reviewed several, then recent books, related to developments in machine learning. This included Pat Langley’s Elements of Machine Learning (Morgan Kaufmann), Tom Mitchell’s Machine Learning (McGraw-Hill), and Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations by Ian Witten and Eibe Frank (Morgan Kaufman) among many others. Dr. Flach mentioned Michael Berry and Gordon Linoff’s Data Mining Techniques for Marketing, Sales, and Customer Support (John Wiley) for it's excellent writing style citing the paragraph below and commending "I wish that all computer science textbooks were written like this."
“People often find it hard to understand why the training set and test set are “tainted” once they have been used to build a model. An analogy may help: Imagine yourself back in the 5th grade. The class is taking a spelling test. Suppose that, at the end of the test period, the teacher asks you to estimate your own grade on the quiz by marking the words you got wrong. You will give yourself a very good grade, but your spelling will not improve. If, at the beginning of the period, you thought there should be an ‘e’ at the end of “tomato”, nothing will have happened to change your mind when you grade your paper. No new data has entered the system. You need a test set!
Now, imagine that at the end of the test the teacher allows you to look at the papersof several neighbors before grading your own. If they all agree that “tomato” has no final ‘e’, you may decide to mark your own answer wrong. If the teacher gives the same quiz tomorrow, you will do better. But how much better? If you use the papers of the very same neighbors to evaluate your performance tomorrow, you may still be fooling yourself. If they all agree that “potatoes” has no more need of an ‘e’ then “tomato”, and you have changed your own guess to agree with theirs, then you will overestimate your actual grade on the second quiz as well. That is why the evaluation set should be different from the test set.” [3, pp. 76–77] 4
That is why when I recently came across "Machine Learning The Art and Science of Algorithms that Make Sense of Data", I decided to check it out and wasn't disappointed. Dr. Flach is the Professor of Artificial Intelligence at the University of Bristol and in this "future classic", he left no stone unturned when it comes to clarity and explainability. The book starts with a machine learning sampler, introduces the ingredients of machine learning fast progressing to Binary classification and Beyond. Written as a textbook, riddled with examples, foot-notes and figures, this text elaborates concept learning, tree models, rule models, linear models, distance-based models, probabilistic models to features and ensembles concluding with Machine learning experiments. I really enjoyed the "Important points to remember" section of the book as a quick refresher on machine-learning-commandments.
The concept learning section seems to have been influenced by author's own research interest and is not discussed in as much details in contemporary machine learning texts. I also found frequent summarization of concepts to be quite helpful. Contrary to it's subtitle and compared to it's counterparts, the book however is light on algorithms and code, possibly on purpose. While it explains the concepts with examples, number of formal algorithms are kept to a minimum. This may aid in clarity and help avoiding recipe-book-syndrome while making it potentially inaccessible to practitioners. Great at basics, the text also falls short on elaboration of intermediate to advance topics such as LDA, kernel methods, PCA, RKHS, and convex optimization. For instance, in chapter 10 "Matrix transformations and decompositions" could have been made an appendix while expanding upon meaningful topics like LSA and use cases of sparse matrix (pg 327). It is definitely not the book's fault; but rather of this reader expecting too much from an introductory text just because author explains everything so well!
As a text book on On the Art and Science of Algorithms, Peter Flach definitely delivers on the promise of clarity, with well chosen illustrations and example based approach. A highly recommended reading for all who would like to understand the principles behind machine learning techniques.
Materials can be downloaded from here which generously include excerpts with background material and literature references, full set of 540 lecture slides in PDF including all figures in the book with LaTeX beamer source of the above.
Going for a little Benoit B. Mandelbrot recursion joke here with the title.
Seth Juarez (github) recently spoke to Pasadena .NET user group on the topic of Practical Machine Learning using nuML. Seth is a wonderful speaker, educator and nuML is an excellent library to get started with machine learning in .NET. His explanations are very intuitive; even for people who have been working in the field for a while. During the talk and follow up discussions, there were various technical references made which went beyond the scope of talk. To be fair with Seth, he covered lot of material in an hour and a half; probably couple of weeks worth in a traditional ML course.
Therefore I decided to provide links to these underlying topics for the benefit of attendees in case anyone is interested in knowing more about them.
- No free lunch in search and optimization
- Probably approximately correct learning
- Kernalized Sorting for NLP Presentation - Paper by Seth
- QP Solver
- NP-Complete Problems
- Intuitive Explanation of Expectation Maximization
- Multi-class classification
- Rosylyn and Roslyn CTP Introduces Interactive Code for C#
- Expando Objects
- Cardinality vs Selectivity
- Microsoft Automatic Graph Layout Library
- Positive Definite Matrix
- Kernel Perceptron in Python
- Perceptrons and Kernels
- math.net numerics
- Matrix Slicing
- Vectors and Matrices
- CodeMash 2013 Repo and readme
- What is EM algorithm?
- k-means clustering
- Clustering Algorithms
- Bag of Words Model
- Cosine similarity vs Hamming distance
- Time series regression and generalized least squares
- Machine Learning Techniques for Stock Prediction
- Causality, Correlation and Browian Motion
Happy Machine Learning!
Last night's LA Machine Learning event on Mining Time Series Data w/ Sylvia Halasz of YP at OpenX Pasadena was quite interesting and well attended. Dr. Halasz spoke about Adaptive Ensemble Kalman Filter and her work on building n-gram correlation with the flu outbreaks. Some of the associated papers follow.
- The ngram chief complaint classifier: A novel method of automatically creating chief complaint classifiers based on international classification of diseases groupings
- Detecting the start of the flu season
- Syndrome Surveillance - CDC
Causality, Probability, and Time - A Temporo-Philosophical Primer to Causal Inference with Case Studies
Causality, Probability and Time by Dr. Samantha Kelinberg is a whirlwind yet original journey of the interdisciplinary study of probabilistic temporal logic and causal inference. Probabilistic causation is a fairly demanding area of study which studies the relationship between cause and effect using the tools of probability theory. Judea Pearl, in his seminal text "Causality: Models, Reasoning, and Inference" refers to this quandary by stating that
(causality) connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity.
Dr. Kelinberg's work provides a balanced introduction to background work on this topic while breaking new grounds on a well-positioned approach of causality based on temporal logic. The envisioning problem is the problem of deducing the set of facts, possibly as the result of our actions leading to the decision problem. This is compounded with finding a timely and useful way to represent our knowledge about time, change, and chance.
In this ~260 page book, Dr. Kelinberg begins with a brief history of causality leading to Probability, logic and probabilistic temporal logic. The author then defines causality from various different facets, proceeding to causality inference, token causality and then finally the case studies. With practical examples and algorithms, author devises simple mathematical tools for analyzing the relationships between causal connections, inference, causal significance, model complexity, statistical associations, actions and observations.
Exploiting the temporal nature of probabilistic events, Dr. Kelinberg's research is a thought provoking and valuable addition to the scientific community interested in learning causal effects and inference with respect to time. Built upon the works of the likes of Heckerman, Breese, Santos and Young, this book will pave the way probabilistic reasoning researchers think about temporal effects on causality for years to come.
David Hume believed that the causes are invariably followed by their effects: "We may define a cause to be an object, followed by another, and where all the objects similar to the first, are followed by objects similar to the second." So, would you like a well written margin-annotation-laden text which provides formal and practical case study based approach to this somewhat abstract concept of causality? Then look no further!
A #NoteToSelf style post regarding collection of bayesian network repositories including but not limited to bnet, net, bif, dsc and rda files.
- GeNIe and SMILE Network Repository
- University of Hebrew Bayesian Network Repository
- DSL lab Network Repository
- Aalborg University Repository
- Norsys Bayes Net Library
- Encog Project - Example Bayesian Networks
As statistician Dennis Lindley famously said, "Inside every nonBayesian there is a Bayesian struggling to get out"; it would be safe to interpolate that Sharon McGrayne's interesting tale of trials and triumph of the Bayes Rule, or more accurately Bayes-Laplace-Price rule, is an excellent historical journey, which may help get your Bayesian out of the closet.
The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy makes for an interesting and captivating read especially considering that writing about history of mathematics and statistics for general audience is a daunting task when compared with relatively popular topics like astronomy or physics. In this easy reading for popular-science audience, author covers over three hundred years of the history behind Bayes rule with its applications and engrossing stories of mathematical luminaries; some of which thought it was a brilliant way to model real-life scenarios while others considered it unscientific, an exercise in futility and vehemently fought against the idea of incorporating prior beliefs. Aside from providing thorough research on the subject matter, this text also delves into significant details about life and works of important scientists, mathematicians and statisticians including but not limited to Turing, Von Neumann, Price, Shannon, Bailey, Laplace, Fisher and Feynman. Regarding modern times, I was delighted to see Daphne Koller and Heckerman's work mentioned as well as the role Bayesian techniques played in contemporary discipline of Machine learning.
Starting with the compelling statement
When the facts change, I change my opinion. What do you do, sir?
—John Maynard Keynes
the ups and downs of adoption of Bayesian rule are listed as different eras and separated out as different parts of the book. The 17 chapters are divided into five parts namely Enlightenment and the anti-Bayesian reaction, Second World War era, the glorious revival, to prove it's worth and finally, victory. Did author do a good job explaining Bayes rule is the point of contention among earlier reviews. I agree that a few more concrete examples with algebraic expressions may have helped better explaining how Bayesian priors and it's mathematical formulation by early luminaries in the field makes it easy to work without complex integrals. However, it is to be noted that this book is not a course in antiquity of causality and inference but rather a study of Bayesian thought through centuries and it's profound impact on science and technology. The book very well covers the advances by 'Bayesian revolution' in variety of fields including medical diagnosis, ecology, geology, computer science, artificial intelligence, machine learning, genetics, astrophysics, archaeology, education performance, sports modeling, and more.
Sharon McGrayne's has picked a very relevant topic for contemporary audience interested in mathematical and computational sciences; making this ~350 page book a very informative, absorbing and pleasurable reading. Although light on technical details, proofs, mathematical equations and problems, this book delivers what it sets to accomplish, to tell the story of Bayes theory. "The theory that would not die" tells the story of a robust idea which is simple, intuitive, unsettling to establishment and yet so resilient that despite of all the criticisms from mainstream frequentists, it stayed alive and well. To quote from the book
"Bayes is still young. Probability did not have any mathematics in it until 1700. Bayes grew up in data- poor and computationally poor circumstances. It hasn't settled down yet. We can give it time. We're just starting."
For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Judea Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first, is a challenging problem. Over the years, Dr. pearl has written significantly on both Art and Science of Cause and Effect. In this book on “Causality: Models, Reasoning and Inference”, the inventor of Bayesian belief networks discusses and elaborates on his earlier workings including but not limited to Reasoning with Cause and Effect, Causal inference in statistics, Simpson's paradox, Causal Diagrams for Empirical Research, Robustness of Causal Claims, Causes and explanations, and Probabilities of causation Bounds and identification.
In these eleven chapters followed by an epilogue, Dr. Pearl’s manuscript postulates representational and computational foundation for the processing of information under uncertainty. It commences with introduction of simpler concepts in Bayesian inference, causality and corresponding proves. However, as text progresses into causal vs. statistical concepts along with theory of inferred causation, the theorems get arduous, somewhat counter-intuitive and the text becomes demanding to keep up. Chapter 3 is an interesting read where causality is discussed in context of philosophy and history. As Dr. Liu states, Judea Pearl’s thesis regarding statistics that it deals with quantitative constructs like mean, variance, correlation, regression, dependence, conditional independence, association, likelihood, collapsibility, risk ratio, odd ratio, marginalization, conditionalization, etc. Meanwhile the causal analysis deals with the topics of randomization, influence, effect, confounding, disturbance, correlation, intervention, explanation and attribution. One of the challenges while following Dr. Pearl’s work is that it abstracts causation discussing it in mathematical and philosophical manner without providing concrete mathematical and computational model for applied research. I believe the book provides great foundation for formal representation of causal analysis and its components, such as do(x) to represent intervention.
Automated Reasoning Group at UCLA has made some strides in this area however the applied research aspects of this formalism still needs to be ‘tightly bound’ by reason of scarcity of empirical evidence for the algorithms in practice.
Recently attended Big Data Event @ Caltech. The topic was Big Data, Big Opportunities: Predicting the Future One Byte at a Time and the panel and speakers didn't disappoint. Following is the slidedeck and pictures from the event.