Publications by academic level

Basic Level

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Statistics Using Excel Succinctly

Learn the ins and outs of Microsoft Excel’s statistical capabilities. Author Charles Zaiontz will help you familiarize yourself with an often overlooked but very powerful set of tools. With Statistics Using Excel Succinctly, you will be able to maximize your Excel skills.

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Defining Artificial Intelligence |🇪🇸 Definiendo Inteligencia Artificial

The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry.

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Medium Level

R0identifier_ 7029b309b114bdb1aa72b6fd0da905f3-Yann LeCun’s Deep Learning Course at CDS

Yann LeCun’s Deep Learning Course at CDS

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

R0_fe33488e78e8d3bac711f1ffb6ea5a48-Bayesian-Data-Analysis-course

Bayesian Data Analysis: book & course

This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics

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The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks.

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Advanced Level

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Tidy Modeling with R

This book provides an introduction to how to use our software to create models. We focus on a dialect of R called the tidyverse that is designed to be a better interface for common tasks using R. If you’ve never heard of or used the tidyverse, Chapter 2 provides an introduction. In this book, we demonstrate how the tidyverse can be used to produce high quality models. The tools used to do this are referred to as the tidymodels packages

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The future of AI

If you wonder what is next in the evolution towards general AI then this session is for you. We have seen some painful failures of artificial intelligence pointing to a lack of ‘common sense’. Are neural networks really the solution we seek or is a new path needed? Find out what IBM Research is cooking in terms of hardware and software in the never ending quest towards General AI.

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Machine Learning from scratch (by Danny Friedman)

This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks.

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Research Level

Fourier Neural Operator for Parametric Partial Differential Equations

Fourier Neural Operator for Parametric Partial Differential Equations

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution.

R0_b6959f4e35b2c24097cf630fa7cea606-Evaluating and Characterizing Human Rationales

Evaluating and Characterizing Human Rationales

Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior.

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