The following is a list of free books on Machine Learning. A Brief Introduction To Neural Networks provides a comprehensive overview of the subject of neural networks and is divided into 4 parts –Part I: From Biology to Formalization -- Motivation, Philosophy, History and Realization of Neural Models,Part II: Supervised learning Network Paradigms, Part III: Unsupervised learning Network Paradigms and Part IV: Excursi, Appendices and Registers. A Course In Machine Learning is designed to provide a gentle and pedagogically organized introduction to the field and provide a view of machine learning that focuses on ideas and models, not on math. The audience of this book is anyone who knows differential calculus and discrete math, and can program reasonably well. An undergraduate in their fourth or fifth semester should be fully capable of understanding this material. However, it should also be suitable for first year graduate students, perhaps at a slightly faster pace.

A data scientist is a person hired to analyze and interpret complicated digital records, together with the utilization statistics of a website; particularly so that it will help an enterprise in its decision-making. An analytical model is a mathematical model that is designed to carry out a particular task or to find out the probability of a selected event i.e. the solution to the equations used to describe modifications in a system can be expressed as a mathematical analytic function. According to Layman, an analytical model is simply a mathematical presentation of an enterprise problem. A simple equation y a bx may be termed as a model with a group of predefined input data and desired output. Scalable and efficient analytical modeling is severely consequential to enable the business to use those techniques to ever-more sizably voluminous data sets for reducing the time taken to carry out these analyses. Accordingly, models are engendered that put into effect key algorithms to determine the solution to our quandary business.

A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.

This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of the minimum redundancy maximum relevance (mRMRe) model. This approach is based on information theory metrics. It is compatible with discrete and continuous data which are discretised using a large choice of possible rules. The two main problems that can be addressed by this package are the selection of the most representative variables for modeling a collection of variables of interest, i.e., dimension reduction, and variable ranking with respect to a set of variables of interest.

Gibbs sampling, as a model learning method, is known to produce the most accurate results available in a variety of domains, and is a de facto standard in these domains. Yet, it is also well known that Gibbs random walks usually have bottlenecks, sometimes termed "local maxima", and thus samplers often return suboptimal solutions. In this paper we introduce a variation of the Gibbs sampler which yields high likelihood solutions significantly more often than the regular Gibbs sampler. Specifically, we show that combining multiple samplers, with certain dependence (coupling) between them, results in higher likelihood solutions. This side-steps the well known issue of identifiability, which has been the obstacle to combining samplers in previous work. We evaluate the approach on a Latent Dirichlet Allocation model, and also on HMM's, where precise computation of likelihoods and comparisons to the standard EM algorithm are possible.

Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.

We consider planning problems for graphs, Markov decision processes (MDPs), and games on graphs. While graphs represent the most basic planning model, MDPs represent interaction with nature and games on graphs represent interaction with an adversarial environment. We consider two planning problems where there are k different target sets, and the problems are as follows: (a) the coverage problem asks whether there is a plan for each individual target set; and (b) the sequential target reachability problem asks whether the targets can be reached in sequence. For the coverage problem, we present a linear-time algorithm for graphs, and quadratic conditional lower bound for MDPs and games on graphs. For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs. Our results with conditional lower bounds establish (i) modelseparation results showing that for the coverage problem MDPs and games on graphs are harder than graphs, and for the sequential reachability problem games on graphs are harder than MDPs and graphs; and (ii) objective-separation results showing that for MDPs the coverage problem is harder than the sequential target problem.

State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many applica- tions, the performance of these methods decays as the forecasting horizon extends beyond these few time steps. This paper aims to explore the challenges of long-horizon forecasting using LSTM networks. Here, we illustrate the long-horizon forecasting problem in datasets from neuroscience and energy supply management. We then propose expectation-biasing, an approach motivated by the literature of Dynamic Belief Networks, as a solution to improve long-horizon forecasting using LSTMs. We propose two LSTM ar- chitectures along with two methods for expectation biasing that significantly outperforms standard practice.

Markus Heinonen 1, 2, Maria Osmala 1, Henrik Mannerstr om 1, Janne Wallenius 3 Samuel Kaski 1, 2, Juho Rousu 1, 2 and Harri L ahdesm aki 1 1 Department of Computer Science, Aalto University, Espoo, 02150, Finland 2 Helsinki Institute for Information Technology, Finland 3 Institute for Molecular Medicine Finland, Helsinki, Finland Abstract Motivation: Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates. Results: We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plugin replacement to conventional metabolic balance methods, such as flux balance analysis (FBA). Our experiments indicate that we can characterise the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in C. acetobutylicum from 13C data than flux variability analysis. Contact: markus.o.heinonen@aalto.fi 1 Introduction Metabolic modelling considers networks of up to thousands of chemical reactions that transform metabolite molecules within cellular organisms (Palsson, 2015). The key problem of metabolism is estimation of the reaction rates, or fluxes, of the system of the highly interdependent intracellular fluxes from measurements of few exchange fluxes that transfer nutrients or products between the external medium and the cell. The dominant approach to flux estimation is the celebrated Flux Balance Analysis (FBA) framework that finds reaction rates that maximise prespecified cellular growth function (Feist and Palsson, 2010), while assuming the cell is in a steady-state, where concentrations of intracellular metabolites do not change (Almaas et al., 2004). The FBA problem can be casted as a convenient and computationally efficient linear programming problem of solving a system of linear steady-state constraints while maximising a linear growth target (Orth et al., 2010), and where flux measurements can be encoded as constraints to the fluxes (Carreira et al., 2014).

This thesis investigates unsupervised time series representation learning for sequence prediction problems, i.e. generating nice-looking input samples given a previous history, for high dimensional input sequences by decoupling the static input representation from the recurrent sequence representation. We introduce three models based on Generative Stochastic Networks (GSN) for unsupervised sequence learning and prediction. Experimental results for these three models are presented on pixels of sequential handwritten digit (MNIST) data, videos of low-resolution bouncing balls, and motion capture data. The main contribution of this thesis is to provide evidence that GSNs are a viable framework to learn useful representations of complex sequential input data, and to suggest a new framework for deep generative models to learn complex sequences by decoupling static input representations from dynamic time dependency representations.