We are going to commence our new tutorial series about Machine Learning. We will use python as programming language for this tutorial series. This article gives a brief introduction to Machine Learning. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." Machine Learning models consists algorithms that can learn and make predictions from data.Machine Learning has been evolved from prediction making and computational learning theory in artificial intelligence.It helps computers to learn and perform a certain task based on past experience.These models can be based on following: In this article we saw a brief introduction to Machine Learning and in next article we will see how to install Anaconda.

In Germany there also exists some manager game similar to fantasy football with soccer. I modeled the team selection as a constrained knapsack problem and optimized the team with an EA/SAT solver (maximize points for a given budget and under the game constraints). Unfortunately, I only have the points from last season and therefore I don't have any meaningful prediction model. Currently, my handpicked team outperforms the optimized team as the optimized team. Need to do some write up...

When building machine learning models, we want to keep error as low as possible. Two major sources of error are bias and variance. If we managed to reduce these two, then we could build more accurate models. But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves. We'll work with a real world data set and try to predict the electrical energy output of a power plant. Some familiarity with scikit-learn and machine learning theory is assumed. If you don't frown when I say cross-validation or supervised learning, then you're good to go. If you're new to machine learning and have never tried scikit, a good place to start is this blog post. We begin with a brief introduction to bias and variance.

Every day, it seems that there is another innovation regarding how artificial intelligence can be used to do things humans couldn't do on their own. Several weeks ago, it was reported that scientists can now use AI to listen to conversations that dolphins are having with each other; an algorithm assists a research team go through millions of echolocation clicks made by these marine mammals found in the Gulf of Mexico. Although this may not seem important, learning about how new innovations with AI sensor technology can sift through ecological data may help to see how climate change and oil spills impact dolphins in this region while being a catalyst to other possible breakthroughs with artificial intelligence. Machine Learning is a component of computer science that allows computers the ability to understand without being programmed explicitly. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data.

We study the $\ell_0$-Low Rank Approximation Problem, where the goal is, given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which $\|A'-A\|_0$ is minimized. Here, for a matrix $B$, $\|B\|_0$ denotes the number of its non-zero entries. This NP-hard variant of low rank approximation is natural for problems with no underlying metric, and its goal is to minimize the number of disagreeing data positions. We provide approximation algorithms which significantly improve the running time and approximation factor of previous work. For $k > 1$, we show how to find, in poly$(mn)$ time for every $k$, a rank $O(k \log(n/k))$ matrix $A'$ for which $\|A'-A\|_0 \leq O(k^2 \log(n/k)) \OPT$. To the best of our knowledge, this is the first algorithm with provable guarantees for the $\ell_0$-Low Rank Approximation Problem for $k > 1$, even for bicriteria algorithms. For the well-studied case when $k = 1$, we give a $(2+\epsilon)$-approximation in {\it sublinear time}, which is impossible for other variants of low rank approximation such as for the Frobenius norm. We strengthen this for the well-studied case of binary matrices to obtain a $(1+O(\psi))$-approximation in sublinear time, where $\psi = \OPT/\nnz{A}$. For small $\psi$, our approximation factor is $1+o(1)$.

We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period. As a bidding strategy, we propose a polynomial-time algorithm, inspired by the dynamic programming approach to the knapsack problem. The proposed algorithm, referred to as dynamic programming on discrete set (DPDS), achieves a regret order of $O(\sqrt{T\log{T}})$. By showing that the regret is lower bounded by $\Omega(\sqrt{T})$ for any strategy, we conclude that DPDS is order optimal up to a $\sqrt{\log{T}}$ term. We evaluate the performance of DPDS empirically in the context of virtual trading in wholesale electricity markets by using historical data from the New York market. Empirical results show that DPDS consistently outperforms benchmark heuristic methods that are derived from machine learning and online learning approaches.

We introduce a collaborative PAC learning model, in which k players attempt to learn the same underlying concept. We ask how much more information is required to learn an accurate classifier for all players simultaneously. We refer to the ratio between the sample complexity of collaborative PAC learning and its non-collaborative (single-player) counterpart as the overhead. We design learning algorithms with O(ln(k)) and O(ln^2(k)) overhead in the personalized and centralized variants our model. This gives an exponential improvement upon the naive algorithm that does not share information among players. We complement our upper bounds with an Omega(ln(k)) overhead lower bound, showing that our results are tight up to a logarithmic factor.

In a regression task, a predictor is given a set of instances, along with a real value for each point. Subsequently, she has to identify the value of a new instance as accurately as possible. In this work, we initiate the study of strategic predictions in machine learning. We consider a regression task tackled by two players, where the payoff of each player is the proportion of the points she predicts more accurately than the other player. We first revise the probably approximately correct learning framework to deal with the case of a duel between two predictors. We then devise an algorithm which finds a linear regression predictor that is a best response to any (not necessarily linear) regression algorithm. We show that it has linearithmic sample complexity, and polynomial time complexity when the dimension of the instances domain is fixed. We also test our approach in a high-dimensional setting, and show it significantly defeats classical regression algorithms in the prediction duel. Together, our work introduces a novel machine learning task that lends itself well to current competitive online settings, provides its theoretical foundations, and illustrates its applicability.

It has been a long-standing problem to efficiently learn a halfspace using as few labels as possible in the presence of noise. In this work, we propose an efficient Perceptron-based algorithm for actively learning homogeneous halfspaces under the uniform distribution over the unit sphere. Under the bounded noise condition~\cite{MN06}, where each label is flipped with probability at most $\eta < \frac 1 2$, our algorithm achieves a near-optimal label complexity of $\tilde{O}\left(\frac{d}{(1-2\eta)^2}\ln\frac{1}{\epsilon}\right)$ in time $\tilde{O}\left(\frac{d^2}{\epsilon(1-2\eta)^3}\right)$. Under the adversarial noise condition~\cite{ABL14, KLS09, KKMS08}, where at most a $\tilde \Omega(\epsilon)$ fraction of labels can be flipped, our algorithm achieves a near-optimal label complexity of $\tilde{O}\left(d\ln\frac{1}{\epsilon}\right)$ in time $\tilde{O}\left(\frac{d^2}{\epsilon}\right)$. Furthermore, we show that our active learning algorithm can be converted to an efficient passive learning algorithm that has near-optimal sample complexities with respect to $\epsilon$ and $d$.

MS or PhD in Mathematics, Physics, Computer Science with a specialization in data analysis/machine learning/data science, strongly preferred Ability to apply a broad range of algorithms to varied data science problems Expertise in machine learning theory and practice with a solid understanding of machine learning algorithms Experience in the following: Applying regression, classification and clustering algorithms to varied types of data Supervised and unsupervised learning Use of data science languages such as R, Python, etc Building and testing predictive models Applying very large amounts of training data sets to train models Working knowledge of various text mining algorithms and their use-cases [e.g., keyword extraction, PLSA, LDA, HMM, CRF, deep learning and recurrent ANN, word2vec/doc2vec and Bayesian modeling] Strong understanding of text pre-processing and normalization techniques, such as tokenization, POS tagging and parsing and how they work at a low level Strong understanding of testing and tuning models