Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.

This book is available free in .PDF format via the link above, and the site offers links to all the lab code. Written by professors at USC, Stanford and the University of Washington and focused on R -- the language of statistical computing that is often used for machine learning and AI programs in this area -- the book has been described as "the'how to' manual for statistical learning." Once you're done with this book, move on to the authors' follow-up, " The Elements of Statistical Learning," also available for free online (although both can be purchased, as well).

Salesforce Artificial Intelligence, Data Science & Data Discovery with Clicks Instead of Code / Salesforce Einstein AI This course is for the absolute beginner to Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science. If you are feeling overwhelmed by either the tsunami of data that you are tasked with trying to make sense out of, or overwhelmed by the tsunami of media coverage around Artificial Intelligence, Deep Learning, Data Science, and Machine Learning, I am here to share a competitive advantage. There is an AI and Data Discovery platform that can be constructed and configured with clicks instead of code. The disruptive power of this is that Artificial Intelligence is now available to the masses, and not just to the quants and data scientists among us. You can now not only catch the competition, but leap frog past them, by leveraging Salesforce Einstein as your On-Demand Data Scientist.

Learn the complete quant trading workflow and use machine learning algortihms to develop good trading strategies. The course is designed to fully immerse you into the complete quantitative trading workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you from zero to hero. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

Twenty years from now we will speak all languages, recognise all faces, remember conversations and diseases that kill people today but will be detected much earlier now, thanks to Artificial Intelligence (AI) powered systems. In 50 years, it might be possible children born then will live to at least 200 years; and climate change will come to a halt! The world will be completely powered by alternate sources of energy instead of burning fossil fuels. In fact, Thrun, 51, who co-founded and runs three startups simultaneously, is working towards some of these goals himself. Udacity is for online learning, offering nano-degrees (short courses) in areas including drones and machine learning; Kitty Hawk Corp is making electric planes and flying cars while AI powered Cresta.ai is trying to automate repetitive jobs.

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.

We propose a developmental approach that allows a robot to interpret and describe the actions of human agents by reusing previous experience. The robot first learns the association between words and object affordances by manipulating the objects in its environment. It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment. It finally fuses the information from these two models to interpret and describe human actions in light of its own experience. In our experiments, we show that the model can be used flexibly to do inference on different aspects of the scene. We can predict the effects of an action on the basis of object properties. We can revise the belief that a certain action occurred, given the observed effects of the human action. In an early action recognition fashion, we can anticipate the effects when the action has only been partially observed. By estimating the probability of words given the evidence and feeding them into a pre-defined grammar, we can generate relevant descriptions of the scene. We believe that this is a step towards providing robots with the fundamental skills to engage in social collaboration with humans.

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What "are" these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be assimilated and regarded as "normal," and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science.

Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility.

Recently, I did the micro course Machine Learning Explainability on kaggle.com. I can highly recommend this course as I have learned a lot of useful methods to analyse a trained ML model. For a brief overview of the topics covered, this blog post will summarize my learnings. The following paragraphs will explain the methods Permutation Importance, Partial Dependence Plots and SHAP Values. I will illustrate the methods using the famous Titanic dataset.