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One of the dreams of the Computing sector is to build an intelligent digital assistant that could serve people according to peoples’ nature. Building this type of intelligent machine is a big challenge to computer scientists. An intelligent machine must have at least the following behaviours – vision, speech and voice recognition, smelling sense, learning from experience to solve new problems and coping with the unknown. The science of artificial intelligence (AI) is trying to overcome these challenges by combining the study of nature, understanding from humans' intelligent behaviour and brain function, other animal’s acute senses, with mathematics, statistics, logic and traditional computer science.
You’ll learn the philosophical background to AI, current trends and the future of AI, ethics and issues in AI ,a range of AI applications (computer vision, speech processing and so forth), top-down approach of AI techniques, fuzzy logic, knowledge-based systems, natural language processing), bottom-up approach of AI techniques (neural networks, evolutionary computing, swarm intelligence), and emerging AI technologies (Brain-Computer Interfacing, Ambient AI, Smart City, GPU AI etc)
You'll gain hands-on experience in developing intelligent systems using a programming language such as C/C++, C#, Java, Prolog, Lisp, Python, R, or a tool such as TensorFlow, Weka, KNIME, MS Azure ML, Accord.NET, AForge.NET, Neuroph, tools for NLP (NLTK, AIML), tools for swarm robotics (Microsoft robotics developer studio, Orocos, ‘Player Stage Gazebo’) etc. You'll learn Machine learning as part of the AI and gain skills in areas such as predictive modelling, speech recognition, object recognition, computer vision, anomaly detection, medical diagnosis and prognosis, robot control, time series forecasting and much more. The course teaches you the basic theory of machine learning, the most efficient machine learning algorithms and the practical implementation of these algorithms. Students will gain hands-on experience in getting these algorithms to solve real-world problems using the foundations of machine learning, types of learning problems (classification, regression, clustering etc.), taxonomy of machine learning algorithms (supervised education, unsupervised learning, reinforcement learning), machine learning algorithms (Decision Tree, Naive Bayes, k-Nearest Neighbour, Support Vector Machine etc.).
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