Putting the ‘G’ in ‘AI’: An Overview of Terms used in (Narrow/Applied) AI- and what they mean to each other”, Suraj Jena, June 10, 2018 “Artificial Intelligence and Machine Learning in the Media Sector”, Fraunhoker Fokus Data scientists and statistical officers combine statistics, mathematics, programming, problem-solving along with the activities of cleansing, preparing, and aligning the data so that it would be useful for everyone including government, scientists, health … Nine terms (36 months) for part-time students. This document presents a review of existing literature on the future of work. If you open up your chrome browser and start typing something, Google immediately provides … There is also sufficient information on Data Science, required skill sets for implement the data science and also brief of machine learning algorithms. The course includes the interviews with industry experts that cover  big data developments in Real Estate, Logistics & Transportation and Healthcare industries. Data Science, Artificial Intelligence, Machine Learning, Deep Learning are the most prominent words which are still sounding weird for many.As we know that the entire world is chasing this technology to grab into its hand. 4. Errors and Exceptions 2 EE0005 Introduction to Data Science and Artificial Intelligence Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Students must complete a 3-day workshop on “Ethics in Data Science and Artificial Intelligence” that will be offered once a year. Admittance will be decided by the Graduate Director on a case-by case basis. It incorporates techniques of statistics and mathematics, such data mining, multivariate data analysis and visualization, along with computer science and even machine learning to draw knowledge from data and provide both insights and decision paths. 78 graduates from 29 countries selected from more than 2,000 applicants The student body, including 14 Emiratis, were given a detailed orientation about the university prior to … Data science includes analyzing data collected from the web, smartphones, customers, sensors, and other sources. With an introduction by Microsoft CEO Satya Nadella, this series of short videos will introduce you to how artificial intelligence works and why it matters. There are no special requirements or prerequisites. According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”.Artificial Intelligence is a Many problems in AI can be solved theoretically by intelligently searching through many possible solutions: Reasoning can be reduced to performing a search. Data science has become a necessary leading technology for combining multiple fields including statistics, scientific methods, and data analysis to extract value from data. The course will motivate you to work closely with data and make data-driven decisions in your field of study. Three terms (12 months) for full-time students. Google’s Search Engine One of the most popular AI Applications is the google search engine. The program information below is valid for the winter 2021 term (January 1, 2021 - April 30, 2021). Students are required to take the following core courses: CS 651 Data-Intensive Distributed Computing (designed for CS major background students), or, CS 631 Data-Intensive Distributed Analytics (designed for non-CS major background students), STAT 841 Statistical Learning - Classification, STAT 844 Statistical Learning - Function Estimation, CS 638 Principles of Data Management and Use, CS 685 Machine Learning: Statistical and Computational Foundations, CO 602 / CS 795 Fundamentals of Optimization, CO 673 / CS 794 Optimization for Data Science. Introduction to Data Science and Skillset required for working with Big Data, Technological Breakthroughs which Enable Big Data Solutions (Connectivity, Cloud, Open Source, Hadoop and NoSQL), Big Data Technology Architecture and most popular technology tools used for each Architecture Layer, Beginner's Introduction to Data Analysis, Artificial Intelligence and Machine Learning, Simplified Overview of Machine Learning Algorithms and Neural Networks, Course overview and Introduction to big data, Big Data in Practice - LOGISTICS & TRANSPORTATION, Logistics & Transportation: Social Impact of Artificial Intelligence & IoT, Logistics & Transportation: Predictive & Prescriptive Maintenance, Logistics & Transportation: Prepositioning of Goods and Just in Time inventory, Logistics & Transportation: Route Optimisation, Logistics & Transportation: Warehouse Optimisation and order picking, Logistics & Transportation: The Future of the industry, Big Data in Practice - PREDICTIVE MAINTENANCE IN MANUFACTURING, Predictive Maintenance in Manufacturing - Case Study SIBUR, Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT, Real Estate: Introduction to big data in real estate, Real Estate: Business Drivers for Using Big Data, Real Estate & Property Management: Technological Enablers, Real Estate: Building Asset Management and Building Information Modelling, Real Estate: Big Data and IoT in Building Maintenance and Management - examples, Additional Resources to Lecture on Smart Buildings, Real Estate: Smart Cities (examples - Los Angeles and Hudson Yards in New York), Real Estate: Smart Technologies Cost and Government Subsidies (example - Norway), Operational Efficiencies and Sustainability, Healthcare: Data Challenges in Healthcare Industry, Healthcare: Transforming Role of AI and Data Measurement Technologies, Healthcare: Artificial Intelligence in Disease Prevention, Healthcare: Artificial Intelligence in Anti-Ageing, Healthcare: AI in Clinical Decision Making and Cancer Treatment, Healthcare: Clash of AI and Traditional Healthcare Science, Healthcare: Final Remarks - Value of Artificial Intellegence to Consumers, Data Science Definition and Required Skillset, Guest Speakers importance of working in teams & understanding business objective, Data Management Technological Breakthroughs (e.g. 1 - INTRODUCTION Welcome to Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence! AU: 3 | Prerequisite: EE1005 | LAMS/TEL(Online Videos & Resources) (16); Example Classes (26 hrs) Course Aims. Progress reports are not required; however, the Director will review students’ overall average every term. Page 1 of 10 11 August 2020 Annex A COURSE CONTENT Academic Year AY2019/20 Semester 1 Author(s) Associate Prof Wang Zhiwei ([email protected]) Course Code CV0003 Course Title Introduction to Data Science and Artificial Intelligence Pre-requisites CV1014 Introduction to Computation Thinking Pre-requisite for Nil No of AUs 3 Contact Hours LECTURES 0 LAMS/TEL (Online Videos and … Faculty of Engineering minimum requirements, general information and regulations section of the Graduate Studies Academic Calendar, Graduate Academic Integrity Module (Graduate AIM), Combinatorics and Optimization (CO) courses, Graduate Studies and Postdoctoral Affairs (GSPA). These courses must normally be taken from the following list of selected graduate courses. 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