Silicon Valley Hands On Programming Events

Probability and Statistics Lectures for Machine Learning and individual projects. This meetup is designed for people wanting a more in depth and more customizable format than online classes or structured graduate level ML classes. This meetup is an extension to undergraduate and graduate ML classes. The instructor is a Stanford Phd and current professor at Tokyo University. The best use of this format is as an extension to online classes from Udacity and Coursera. Individuals can choose to listen to the lectures, suggest new topics and pursue individual projects. Current resouces associated with this meetup are: piazza discussion group for Q/A and assignments for this meetup which supplement the lecture topics below, a slack group and an email list. Other meetups offered by this professor include an intro to DL and an advanced DL. These meetups include a range of participants ranging from other Professors to individuals implementing current papers in Deep Learning and current beginner students. Please contact Professor Fabinger directly at [masked] to discuss your individual situation and to sign up to the piazza group, slack group and email list. There is no video for this class. Lectures are conduced via zoom. https://zoom.us/j/768908688 There are corresponding in person lectures in Tokyo University Japan with remote presence every Sat 3:30pm-6pm JST. Please email [masked] for a link and to enroll for these groups. This is not a substitute for online classes or undergraduate/graduate statistics classes but a supplement with the added advantage of individual customiztion and ability to deep dive into topics of interest to the individual through self directed projects. Current Syllabus; First 9 topics covered as of 1/8/2020. Probability and Statistics 1) populations vs. samples 2) exploratory data analysis 3) outcome spaces, classes of events, probability measures 4) definition of random variables as functions on the outcome space 5) discrete, continuous, and mixed probability distributions 6) probability measures for discrete and continuous events 7) Lebesgue measure, Lebesgue integral 8) expectations of random variables 9) expectation, variance, skewness, kurtosis 10) joint distributions and marginal distributions 11) covariance and correlation 12) independence of random variables 13) linear predictors, inner product between random variables and its relationship to the scalar product of features in observed data 14) orthogonality between random variables, its implications, and related usual misconceptions 15) conditional independence of random variables 16) law of large numbers 17) hypothesis testing 18) probability distribution similarity measures: entropy-based divergences, Wasserstein distance, Frechet distance 19) conditional expectations and their relationship to linear predictors, law of iterated expectations 20) properties of regression functions 21) omitted variable bias and its relationship to causal relations between variables, strategies for mitigating omitted variable bias 22) residual regressions 23) discrete regressions, dummy variables 24) average partial effects 25) fixed-effects regressions 26) random-effects regressions 27) entity embeddings 28) regression discontinuity methods

Probability and Statistics Lectures for Machine Learning and individual projects. This meetup is designed for people wanting a more in depth and more customizable format than online classes or structured graduate level ML classes. This meetup is an extension to undergraduate and graduate ML classes. The instructor is a Stanford Phd and current professor at Tokyo University. The best use of this format is as an extension to online classes from Udacity and Coursera. Individuals can choose to listen to the lectures, suggest new topics and pursue individual projects. Current resouces associated with this meetup are: piazza discussion group for Q/A and assignments for this meetup which supplement the lecture topics below, a slack group and an email list. Other meetups offered by this professor include an intro to DL and an advanced DL. These meetups include a range of participants ranging from other Professors to individuals implementing current papers in Deep Learning and current beginner students. Please contact Professor Fabinger directly at [masked] to discuss your individual situation and to sign up to the piazza group, slack group and email list. There is no video for this class. Lectures are conduced via zoom. https://zoom.us/j/768908688 There are corresponding in person lectures in Tokyo University Japan with remote presence every Sat 3:30pm-6pm JST. Please email [masked] for a link and to enroll for these groups. This is not a substitute for online classes or undergraduate/graduate statistics classes but a supplement with the added advantage of individual customiztion and ability to deep dive into topics of interest to the individual through self directed projects. Current Syllabus; First 9 topics covered as of 1/8/2020. Probability and Statistics 1) populations vs. samples 2) exploratory data analysis 3) outcome spaces, classes of events, probability measures 4) definition of random variables as functions on the outcome space 5) discrete, continuous, and mixed probability distributions 6) probability measures for discrete and continuous events 7) Lebesgue measure, Lebesgue integral 8) expectations of random variables 9) expectation, variance, skewness, kurtosis 10) joint distributions and marginal distributions 11) covariance and correlation 12) independence of random variables 13) linear predictors, inner product between random variables and its relationship to the scalar product of features in observed data 14) orthogonality between random variables, its implications, and related usual misconceptions 15) conditional independence of random variables 16) law of large numbers 17) hypothesis testing 18) probability distribution similarity measures: entropy-based divergences, Wasserstein distance, Frechet distance 19) conditional expectations and their relationship to linear predictors, law of iterated expectations 20) properties of regression functions 21) omitted variable bias and its relationship to causal relations between variables, strategies for mitigating omitted variable bias 22) residual regressions 23) discrete regressions, dummy variables 24) average partial effects 25) fixed-effects regressions 26) random-effects regressions 27) entity embeddings 28) regression discontinuity methods

Deep learning is evolving quickly. Important new developments are appearing daily. This group attempts to keep up by reading and discussing current deep learning literature. This meetup uses discussion among the participants to speed understanding of current research results. That requires that some participants read the paper before attending. Anyone is welcome to attend and listen without reading the paper. If nobody reads the paper the meeting will be short. Papers that we're reading, code that participants generate and other random stuff can be found at github site for the group. mike-bowles/hdDeepLearningStudy (https://github.com/mike-bowles/hdDeepLearningStudy) https://arxiv.org/abs/2001.04451 - Reformer, the efficient transformer https://ai.googleblog.com/2020/01/reformer-efficient-transformer.html

Probability and Statistics Lectures for Machine Learning and individual projects. This meetup is designed for people wanting a more in depth and more customizable format than online classes or structured graduate level ML classes. This meetup is an extension to undergraduate and graduate ML classes. The instructor is a Stanford Phd and current professor at Tokyo University. The best use of this format is as an extension to online classes from Udacity and Coursera. Individuals can choose to listen to the lectures, suggest new topics and pursue individual projects. Current resouces associated with this meetup are: piazza discussion group for Q/A and assignments for this meetup which supplement the lecture topics below, a slack group and an email list. Other meetups offered by this professor include an intro to DL and an advanced DL. These meetups include a range of participants ranging from other Professors to individuals implementing current papers in Deep Learning and current beginner students. Please contact Professor Fabinger directly at [masked] to discuss your individual situation and to sign up to the piazza group, slack group and email list. There is no video for this class. Lectures are conduced via zoom. https://zoom.us/j/768908688 There are corresponding in person lectures in Tokyo University Japan with remote presence every Sat 3:30pm-6pm JST. Please email [masked] for a link and to enroll for these groups. This is not a substitute for online classes or undergraduate/graduate statistics classes but a supplement with the added advantage of individual customiztion and ability to deep dive into topics of interest to the individual through self directed projects. Current Syllabus; First 9 topics covered as of 1/8/2020. Probability and Statistics 1) populations vs. samples 2) exploratory data analysis 3) outcome spaces, classes of events, probability measures 4) definition of random variables as functions on the outcome space 5) discrete, continuous, and mixed probability distributions 6) probability measures for discrete and continuous events 7) Lebesgue measure, Lebesgue integral 8) expectations of random variables 9) expectation, variance, skewness, kurtosis 10) joint distributions and marginal distributions 11) covariance and correlation 12) independence of random variables 13) linear predictors, inner product between random variables and its relationship to the scalar product of features in observed data 14) orthogonality between random variables, its implications, and related usual misconceptions 15) conditional independence of random variables 16) law of large numbers 17) hypothesis testing 18) probability distribution similarity measures: entropy-based divergences, Wasserstein distance, Frechet distance 19) conditional expectations and their relationship to linear predictors, law of iterated expectations 20) properties of regression functions 21) omitted variable bias and its relationship to causal relations between variables, strategies for mitigating omitted variable bias 22) residual regressions 23) discrete regressions, dummy variables 24) average partial effects 25) fixed-effects regressions 26) random-effects regressions 27) entity embeddings 28) regression discontinuity methods