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Notify me. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Features: provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools; contains numerous examples and definitions throughout the text; presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks; covers the latest approaches, including ant colony optimization and probabilistic graphical models; written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry.

Role of Data Science in Artificial Intelligence

Other books in this series. Description Table of Contents Product Details Click on the cover image above to read some pages of this book! Industry Reviews "It is a great book, very well written, that presents solid content in a very rigorous theoretical and practical way and provides an excellent methodological guide to the area of computational intelligence --one that could be qualified as a 'must' for the library of any student, professor, researcher or professional in that area. Superintelligence Paths, Dangers, Strategies. In Stock. This is Service Design Doing.

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This course aims to provide an introductory but broad perspective of machine learning fundamental methodologies, and show how to apply machine learning techniques to real-world applications. It is relevant for anyone pursuing a career in AI or Data Science. The teaching content of this course includes different machine learning methodologies in various machine learning paradigms, such as supervised learning, semi-supervised learning, unsupervised learning, etc. Deep learning has recently introduced a paradigm shift from human-design features to end-to-end systems, and has revolutionized several fields including computer vision, speech recognition, and natural language processing.

Top IT companies like Google, Facebook, Microsoft, Apple, Amazon have been actively redesigned their products with deep learning techniques, and the impacts in the coming decades will go beyond self-driving cars, strategic games like Go, and MRI cancer detection. The main objective of this course is to introduce the mathematical foundations, the state-of-the-art architectures, and a professional library of deep learning architectures.

Students will learn how to design their own artificial neural network to solve their data analysis task.

A Methodological Introduction

They will also learn how to code efficiently these new algorithms using PyTorch, one of the most powerful libraries in this field. This course aims to provide the appropriate mathematical background to students who will study other courses in the Master of Science in Artificial Intelligence programme. Upon completion of this subject, the student should be able to:.

This course aims to provide appropriate computing background to students who will study other courses in Data Science and AI. Computer vision has been attracting increasing interests thanks to the recent advances in deep learning especially in convolutional neural networks, recurrent neural networks, etc.

Top IT companies around the world such as Google, Apple, Alibaba, Amazon, Tencent and Baidu have invested and will continue to invest heavily in various computer vision technologies due to demands in a wide spectrum of applications and domains such as robotics, autonomous driving, surveillance and security, computer-aided medical diagnosis, etc. The main objective of this course is to introduce the relevant mathematical foundations for computer vision and machine learning, the basic image analytics and machine learning technology in computer vision, and the computer vision technologies in detection, recognition and classification tasks.

A series of real-world problems and challenges will be presented throughout the course via case studies and projects.

Students will have a good understanding of various machine learning and computer vision technologies at the end of this course. They will also learn how to design their own machine learning and computer vision systems for various real-world problems. This course covers fundamental techniques to manage and process text data. The main topics include: 1 text indexing and search: inverted index, query processing, ranking, and evaluation, 2 word-level, sentence-level, document-level, and collection-level processing: morphological analysis, part-of-speech tagging, parsing, summarization, classification and clustering, and topic modeling, and 3 case studies and applications: social media text, sentiment analysis, and information extraction.

Many of the complex systems are dynamic systems in which their states change over time. This course introduces time series models and the corresponding methods for data analysis and inference. Topics include regression models, autoregressive AR , moving average MA , ARMA, and ARIMA processes, stationary and non- stationary processes, seasonal processes, identification of models, estimation of parameters, diagnostic checking of fitted models, rare event detection, forecasting, spectral analysis and time series models of heteroscedasticity.

Real world applications for understanding characteristics, modelling and evaluating forecasts of time series data in economics, finance and industries are elaborated with lab on using R. We begin with a short introduction of learning, adaptation andcognition: examining: learning forms, supervised, unsupervised, reinforcement; Adaptation mechanism - error driven, performance objective; cognitive knowledge representation and reasoning for rational decision making. The tools for performing such learning via evolutionary and neural computations are examined.

Cognitive organisation of knowledge and the integration of such structures with soft computing models are also discussed.


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