Curriculum


New blood test can detect 50 types of cancer

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A new blood test that can detect more than 50 types of cancer has been revealed by researchers in the latest study to offer hope for early detection. The test is based on DNA that is shed by tumours and found circulating in the blood. More specifically, it focuses on chemical changes to this DNA, known as methylation patterns. Researchers say the test can not only tell whether someone has cancer, but can also shed light on the type of cancer they have. Dr Geoffrey Oxnard of Boston's Dana-Farber Cancer Institute, part of Harvard Medical School, said the test was now being explored in clinical trials.


AI Is Changing Work -- and Leaders Need to Adapt

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As AI is increasingly incorporated into our workplaces and daily lives, it is poised to fundamentally upend the way we live and work. Concern over this looming shift is widespread. A recent survey of 5,700 Harvard Business School alumni found that 52% of even this elite group believe the typical company will employ fewer workers three years from now. The advent of AI poses new and unique challenges for business leaders. They must continue to deliver financial performance, while simultaneously making significant investments in hiring, workforce training, and new technologies that support productivity and growth.


Programming Machine Learning: From Coding to Deep Learning by Paolo Perrotta

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Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.


Top Resources to Kick off Your 2020 Data Science Learning Path

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"Listening to the data is important… but so is experience and intuition. After all, what is intuition at its best but large amounts of data of all kinds filtered through a human brain rather than a math model?" One of the most important steps as Data Science is a quantitative domain and core mathematical foundations will serve as a base for your learning. Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure the likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.


Teaching 'common sense' to artificial intelligence

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Ever wonder why virtual assistant Siri can easily tell you what the square root of 1,558 is in an instant but can't answer the question "what happens to an egg when you drop it on the ground?" Artificial intelligence (A.I.) interfaces on devices like Apple's iPhone or Amazon's Alexa often fall flat on what many people consider to be basic questions, but can be speedy and accurate in their responses to complicated math problems. That's because modern A.I. currently lacks common sense. "What people who don't work in A.I. everyday don't realize is just how primitive what we call'A.I.' is nowadays," machine-learning researcher Alan Fern of Oregon State University's College of Engineering told KOIN 6 News. "We have A.I.s that do very specialized, specific things, specific tasks, but they're not general purpose. They can't interact in general ways because they don't have the common sense that you need to do that."


Machines learn chemistry to predict reaction results

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"A chemical reaction is a highly complex system", explains Frederik Sandfort, PhD student at the Institute of Organic Chemistry and one of the lead authors of the publication. "In contrast to the prediction of properties of individual compounds, a reaction is the interaction of many molecules and thus a multidimensional problem," he adds. Moreover, there are no clearly defined "rules of the game" which, as in the case of modern chess computers, simplify the development of AI models. For this reason, previous approaches to accurately predicting reaction results such as yields or products are mostly based on a previously gained understanding of molecular properties. "The development of such models involves a great deal of effort. Moreover, the majority of them are highly specialized and cannot be transferred to other problems," Frederik Sandfort adds.


My Journey from Physics into Data Science

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I still learn new knowledge everyday with my growing passion in Data Science field. To pursue different career track as a graduating physics student there must be'Why' and'How' questions to be answered. Having been asked by a number of people about my transition from academia -- Physics to Data Science, I hope my story could answer the questions on why I decided to become a Data Scientist and how I pursued the goal, and ultimately encourage as well as inspire more people to pursue their passion. The CERN Summer Student Programme offers once-in-a-lifetime opportunity for undergraduate students of physics, computing and engineering to join one of their research projects with top scientists in multicultural teams at CERN in Geneva, Switzerland. In June 2017, I was very fortunate to be accepted to join the programme.


Random Projections for Manifold Learning

Neural Information Processing Systems

We propose a novel method for {\em linear} dimensionality reduction of manifold modeled data. First, we show that with a small number $M$ of {\em random projections} of sample points in $\reals N$ belonging to an unknown $K$-dimensional Euclidean manifold, the intrinsic dimension (ID) of the sample set can be estimated to high accuracy. Second, we rigorously prove that using only this set of random projections, we can estimate the structure of the underlying manifold. In both cases, the number random projections required is linear in $K$ and logarithmic in $N$, meaning that $K M\ll N$. To handle practical situations, we develop a greedy algorithm to estimate the smallest size of the projection space required to perform manifold learning.


Episodic Memory in Lifelong Language Learning

Neural Information Processing Systems

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier. We propose an episodic memory model that performs sparse experience replay and local adaptation to mitigate catastrophic forgetting in this setup. Experiments on text classification and question answering demonstrate the complementary benefits of sparse experience replay and local adaptation to allow the model to continuously learn from new datasets. We also show that the space complexity of the episodic memory module can be reduced significantly ( 50-90%) by randomly choosing which examples to store in memory with a minimal decrease in performance. We consider an episodic memory component as a crucial building block of general linguistic intelligence and see our model as a first step in that direction.


No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

Neural Information Processing Systems

Nonlinear embedding manifold learning methods provide invaluable visual insights into a structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their embedding quality can be poor. We propose a natural extension to several manifold learning methods aimed at identifying pressured points, i.e. points stuck in the poor local minima and have poor embedding quality. We show that the objective function can be decreased by temporarily allowing these points to make use of an extra dimension in the embedding space. Our method is able to improve the objective function value of existing methods even after they get stuck in a poor local minimum.