Machine Learning training bootcamp is a 3-day specialized training course that covers the essentials of machine learning, a shape and utilization of man-made reasoning (AI). Machine learning computerizes the information investigation process by empowering PCs, machines and IoT to learn and adjust through experience connected to particular undertakings without unequivocal programming. Learning Objectives: Learn about Artificial Intelligence and Machine Learning List similarities and differences between AI, Machine Learning and Data Mining Learn how Artificial Intelligence uses data to offer solutions to existing problems Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn, adapt and optimize / Clarify how Data Mining can serve as foundation for AI and machine learning to use existing information to highlight patterns List the various applications of machine learning and related algorithms Learn how to classify the types of learning such as supervised and unsupervised learning Implement supervised learning techniques such as linear and logistic regression Use unsupervised learning algorithms including deep learning, clustering and recommender systems (RS) used to help users find new items or services, such as books, music, transportation, people and jobs based on information about the user or the recommended item Learn about classification data and Machine Learning models Select the best algorithms applied to Machine Learning Make accurate predictions and analysis to effectively solve potential problems List Machine Learning concepts, principles, algorithms, tools and applications Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering Comprehend the theoretical concepts and how they relate to the practical aspects of machine learning / Be able to model a wide variety of robust machine learning algorithms including deep learning, clustering and recommendation systems Course Agenda and Topics: The Basics of Machine Learning Machine Learning Techniques, Tools and Algorithms Data and Data Science Review of Terminology and Principles Applied Artificial Intelligence (AI) and Machine Learning Popular Machine Learning Methods Learning Applied to Machine Learning Principal component Analysis Principles of Supervised Machine Learning Algorithms Principles of Unsupervised Machine Learning Regression Applied to Machines Learning Principles of Neural Networks Large Scale Machine Learning Introduction to Deep Learning Applying Machine Learning Overview of Algorithms Overview of Tools and Processes Request More Information .

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.

This work develops novel strategies for optimal planning with semantic observations using continuous state Partially Observable Markov Decision Processes (CPOMDPs). Two major innovations are presented in relation to Gaussian mixture (GM) CPOMDP policy approximation methods. While existing methods have many theoretically nice properties, they are hampered by the inability to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes GM approximations of Point-Based Value Iteration Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. A key benefit of this approach is that dynamic decision-making tasks can be performed with complex non-Gaussian uncertainties, while also exploiting continuous dynamic state space models (thus avoiding cumbersome and costly discretization). The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state of the art GM and Monte Carlo based policy approximations, but require significantly less modeling overhead and runtime cost. Additional results demonstrate the robustness of this approach to model errors.

Fuzzing is a commonly used technique designed to test software by automatically crafting program inputs. Currently, the most successful fuzzing algorithms emphasize simple, low-overhead strategies with the ability to efficiently monitor program state during execution. Through compile-time instrumentation, these approaches have access to numerous aspects of program state including coverage, data flow, and heterogeneous fault detection and classification. However, existing approaches utilize blind random mutation strategies when generating test inputs. We present a different approach that uses this state information to optimize mutation operators using reinforcement learning (RL). By integrating OpenAI Gym with libFuzzer we are able to simultaneously leverage advancements in reinforcement learning as well as fuzzing to achieve deeper coverage across several varied benchmarks. Our technique connects the rich, efficient program monitors provided by LLVM Santizers with a deep neural net to learn mutation selection strategies directly from the input data. The cross-language, asynchronous architecture we developed enables us to apply any OpenAI Gym compatible deep reinforcement learning algorithm to any fuzzing problem with minimal slowdown.

Part of Speech Tagging (POS) is a process of tagging sentences with part of speech such as nouns, verbs, adjectives and adverbs, etc. Hidden Markov Models (HMM) is a simple concept which can explain most complicated real time processes such as speech recognition and speech generation, machine translation, gene recognition for bioinformatics, and human gesture recognition for computer vision, and more. In this post, we will use the Pomegranate library to build a hidden Markov model for part of speech tagging. We will not go into the details of statistical part-of-speech tagger. However, if you are interested, here is the paper. The data is a copy of the Brown corpus and can be found here.

Popular libraries make up the backbone of data science: scikit-learn, TensorFlow, Caffe, and Keras are the standard Python choices. But these libraries don't tend to implement niche techniques (scikit-learn's policy actually states that they don't consider algorithms less than three years old or with less than 200 citations!), Enter mlpack: a flexible, fast machine learning library. It's written in C, with bindings to Python and command-line programs that can be used for simpler data science tasks. Because of its use of templates for configurability, it is easy to customize the specific behavior of algorithms without any runtime penalty.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

An empirical investigation of active/continuous authentication for smartphones is presented in this paper by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Variations of Hidden Markov Models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation does not depend on the top N-apps, rather uses the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for simple sequence matching. It is found that for enhanced verification performance, unforeseen events should be incorporated in the models by adopting smoothing techniques with HMMs. For validation, extensive experiments on two distinct datasets are performed. The marginal smoothing technique is the most effective for user verification in terms of equal error rate (EER) and with a sampling rate of 1/30s^{-1} and 30 minutes of historical data, and the method is capable of detecting an intrusion within ~2.5 minutes of application use.

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.

In this paper we show that restricting the representation-layer of a Recurrent Neural Network (RNN) improves accuracy and reduces the depth of recursive training procedures in partially observable domains. Artificial Neural Networks have been shown to learn useful state representations for high-dimensional visual and continuous control domains. If the the tasks at hand exhibits long depends back in time, these instantaneous feed-forward approaches are augmented with recurrent connections and trained with Back-prop Through Time (BPTT). This unrolled training can become computationally prohibitive if the dependency structure is long, and while recent work on LSTMs and GRUs has improved upon naive training strategies, there is still room for improvements in computational efficiency and parameter sensitivity. In this paper we explore a simple modification to the classic RNN structure: restricting the state to be comprised of multi-step General Value Function predictions. We formulate an architecture called General Value Function Networks (GVFNs), and corresponding objective that generalizes beyond previous approaches. We show that our GVFNs are significantly more robust to train, and facilitate accurate prediction with no gradients needed back-in-time in domains with substantial long-term dependences.