Prentice-Hall, 2002. ISE is a set of modern Systems Engineering areas with various interrelations. endobj 8. <> ITS is an international program intended to improve the effectiveness and efficiency of surface transportation systems through advanced technologies in information systems, communications, and sensors. Course outcomes: Upon successful completion of this course, the student shall be able to: 1) Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations. <> This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. We aim to bring both the course description and the semester page of all courses up to date with correct information by 1 February 2021. We use cookies to improve your experience on our site. 9 0 obj endobj • Intellectual Skills: B.1 (Modelling) B.4 (Criteria Evaluation and Testing). 9. S. Pinker. A*, interative deepening), logic, planning, knowledge representation, machine learning, and applications from areas such as computer vision, robotics, natural language processing, and expert systems. 6 0 obj ... Social Media and Intelligent Systems. The course starts off with introducing you to data science, where you will learn that data science is an interdisciplinary field that uses scientific processes and systems to extract knowledge or insights from data in its various forms. Explain what constitutes "Artificial" Intelligence and how to identify systems with Artificial Intelligence. In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. %���� 8.4 Explain the differences between the major kinds of machine learning problems – namely supervised learning, unsupervised learning and reinforcement learning – and describe the basic ideas of algorithms for solving those problems. 13 0 obj Private study hours:128 endstream <>/XObject<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 960 540] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> %PDF-1.5 You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. Outcome 12.5 is related to the following Computer Science programme outcomes: Course Description. in Computer Science and Engineering (Artificial Intelligence) program … S.J. Apply different AI/IA algorithms to … Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis Wenting Ma Simon Fraser University Olusola O. Adesope Washington State University John C. Nesbit and Qing Liu Simon Fraser University Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. 4 0 obj Unit Learning Outcomes (ULO) Students who successfully complete this unit will be able to: 1. ... Research has found “g” to be highly correlated with many important social outcomes and is the single best predictor of successful job performance. The intended generic learning outcomes. However, courses, services and other matters may be subject to change. Over the last century or so, intelligence has been defined in many different ways. stream ... learn about how intelligent systems use uncertainty in reasoning and decision making in this free online course. CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course also aims to give an overview of the historical, philosophical, and logical foundations of AI.

9.1 Discuss and give examples of the role of analogy and metaphor in science and engineering; $.' L1, L2 Programming assignments are an integral part of the course. Course description. 12 0 obj Bio-inspired intelligent systems have thousands of useful applications in fields as diverse as control theory, telecommunications, music and art. Total contact hours: 22 • Intellectual Skills: B.4 (Criteria Evaluation and Testing). The course topics will vary each year, dependent on available teachers and scientific interests. "How the Mind Works", W.W. Norton & Company, 1999. • Knowledge and Understanding of: A.2 (Software), A.4 (Practice) and A.5 (Theory). At the end of the course, you'll be able to: - make the right choice for your own project when it comes to the target market, parallel executions, time and the lifecycle of your system - hack, avoid failure and promote success - decide whether to buy or to build components - how to assemble a good team - install case tools - learn how to work with SysML This is an introductory course. • is a set of outcomes •F is a set of events •P: F [0,1] is a function that assigns probabilities to events Note: F is a ¾-field, i.e., collection of subsets of such that –If A 2Fthen Ac 2F –If A i 2Fis a countable sequence of sets then [i A i 2F Prof. Songhwai Oh Introduction to Intelligent Systems 4 You learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. COMP5200: Further Object-Oriented Programming. 8.6 Describe how various intelligent-system techniques have been used in the context of several case studies, and compare different techniques in the context of those case studies. <> Robotics and Intelligent Systems, MAE 345, provides students with a working knowledge of methods for design and analysis of robotic and intelligent systems. stream Course Objectives: The main objective of this course is to : Provide a general introduction to intelligent systems . Some IDEATE courses and some SCS undergraduate and graduate courses might not be allowed based on course content. This course will introduce the basic game‐playing techniques such as minimax search and alpha‐beta pruning. <> 8.2 Describe the main kinds of state-space search algorithms, discussing their strengths and limitations. Outcomes 12.3 and 12.4 are related to the following Computer Science programme outcomes: • Knowledge and Understanding of: A.2 (Software), A.4 (Practice) and A.5 (Theory). Please read our full disclaimer. 8.3 Explain the main concepts and principles associated with different kinds of knowledge representation, such as logic, case-based representations, and subsymbolic/connectionist representations. The focus of this course is on core AI techniques for search, knowledge representation and reasoning, planning, and designing intelligent agents. endobj purpose of this course is to familiarize you with the basic techniques of artificial intel- ligence/intelligent systems. Intelligent Systems - ITCS 6150/8150 Chapter 1 Artificial Intelligence Dr. Dewan Tanvir Ahmed Department The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For example, consider a robot in maze that has no prior knowledge about the maze layout. Course content. 'Mathematics for Intelligent System 1' is a course offered in the first semester of B. This course provides a broad introduction and details of faculty research areas. <> Outcomes 12.1-12.2 are related to the following Computer Science programme outcomes: 13.1 Main assessment methods Possible topics include: Introduction to artificial intelligence and intelligent agents Problemsolving and search methods Knowledge, reasoning, and planning (KRP) Social Sciences Undergraduate Stage 2 & 3. Business intelligence (BI) is a technology-driven process for analyzing data and presenting useful information to help executives, managers and other end users make informed business decisions. 2 0 obj endobj (NOTE: The following undergraduate courses do NOT count as Computer Science electives: 02-201, 02-223, 02-250, 02-261, 11-423, 15-351, 16-223, 17-200, 17-333, 17-562. 8 0 obj A. Cawsey, "The Essence of Artificial Intelligence", Prentice-Hall, 1998. Course Outcomes: Upon completion of the course students will be able to: SN Course Outcomes Cognitive levels of attainment as per Bloom’s Taxonomy 1 Understand different types of AI agents. The intended subject specific learning outcomes. 2 hour unseen written examination (50%) P. Bentley. Course Description. x��V]o�0}����v%Ǚ�J��ݐ�����)�4$]���� t*k4Zi\ۉϹ�>�^��q8�΀����Y~t�q΅��?s�\��I�G��K'��a��b���_�u&a�s��c'�� R&-8�AǬ��8j��|�"��x��q'/H?Q��x� @Kǜ+&,��-Yx��4PΚz�5��N*�UdU�@�&7DЮ$7��������S�ڃW�q��^��E��Q��A:ȫtN5�gT�Y�W�G�E^����h�����P�I/�����S?��TY��{h鶴$Ȉ�n���T���nia�}�9S^�r�wφ�UI�$�=5�0@v��0$Yf���;5��wY� �Q���X��A+�d{�՝7����j�ʪ��2�q�cڵ�!�]�L���C� J�-�~RK�r�U���h\k��j�!fQk�E9Mrh�1�Uv�L*�WU��!��uxZTU�� ���4�JfY��#����]�]EQ�e[ݽi�]��n�y�rK���G��z�H�g�Oђh7"#�5�,��K,�aR��r�� �9�}� �5r�x�~s[RWs���+��o�*Z�E+���y'��ɉ�=YӮv� 7�f�ބ���&v��ڽ�r�t�)�&��χ�9���&b�%a_��Rk_�5���x��c[��ߡ�� |�x �`��R�଀�Ţ��M}o���9&cP��5o����9[��r��c���~_c�"pF�&Xh��/��6�J�)�����Vc�F�K�߱�`a Lectures: 45 hours/semester, 3 hours/week. Russell & P. Norvig, "Artificial Intelligence: a modern approach", 2nd Edition. 2. Prof. Songhwai Oh Introduction to Intelligent Systems 11 Performance of a greedy ADP agent that executes the action recommended by the optimal policy for the learned model (one‐step look‐ahead). • Subject-Specific Skills: B.7 (Computational thinking), C.1 (Design and Implementation), C.14 (Identify and develop solutions for computational problems requiring machine intelligence) and D.2 (Evaluation). <> <>>> A2 – Practical assignement (25%) endobj ���� JFIF � � �� ZExif MM * J Q Q Q �� ���� C • Intellectual Skills: B.4 (Criteria Evaluation and Testing). Outcome 12.5 is related to the following Computer Science programme outcomes: On successfully completing the module students will be able to: 11 0 obj 7 0 obj Introduction to Intelligence. “Artificial Intelligence -A Modern Approach” by S. Russell and Peter Norvig, prentice-Hall. Academic Honesty: Cheating in this course will not be tolerated. endobj • Knowledge and Understanding of: A.2 (Software), A.4 (Practice) and A.5 (Theory). <> In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms. endobj University of Kent makes every effort to ensure that module information is accurate for the relevant academic session and to provide educational services as described. <> A1 – Practical assignement (25%) endobj Artificial intelligence is the science that studies and develops methods of making computers more /intelligent/. A selection of topics will be made public at the start of the semester. See general guidelines for examination at the MN Faculty autumn 2020. 5 0 obj 6. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. 3 0 obj • Transferable Skills: D.3 (Information Technology) and D.5 (self-management). Offered by IBM. Several algorithms and methods are discussed, including evolutionary algorithms. endobj ABET Criteria covered: B, C, G and I. Explain a range of techniques of intelligent systems across artificial intelligence (AI) and intelligent agents (IA); both from a theoretical and a practical perspective. <> endobj Course Objective: To make students understand and explore the techniques underlying the design of Intelligent Systems. Learn about how artificial intelligence is used to tackle complex real world problems like speech recognition and machine translations using machine techniques. Embedded Systems are at the heart of almost all modern technologies; Smart Phones to televisions, cars to intelligent light bulbs. Outcomes 12.3 and 12.4 are related to the following Computer Science programme outcomes: 13.2 Reassessment methods Machine learning is concerned with the question of how to make computers learn from experience. ",#(7),01444'9=82. This course considers ITS as a lens through which one can view many transportation and societal issues. Intelligent Transportation Systems (ITS) represent a major transition in transportation on many dimensions. 10 0 obj The module also provides an introduction to both machine learning and biologically inspired computation. Defining Intelligence. View Chapter 1 - Introduction.pptx from ITCS 6150 at University of North Carolina, Charlotte. This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. This course provides an introduction to Intelligent Systems Engineering and an overview of the various degree specializations that are available. Artificial intelligence (AI) and machine learning (ML) are about creating intelligent systems – systems that perceive and respond to the world around them. 9.5 use the library and appropriate internet resources in support of learning. Particular attention is given to modeling dynamic systems, measuring and controlling their behavior, and making decisions about future courses of action. This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. endobj <> 1 0 obj ((CSCI-261 and MATH-251) or permission of instructor) Course Outcomes Tech. This course gives a basic introduction to machine learning (ML) and artificial intelligence … Finds a policy that reaches (4,3) via (2,1), (3,1), (3,2), (3,3) Suboptimal policy "Digital Biology", Simon & Schuster, 2002, See the library reading list for this module (Canterbury). • Intellectual Skills: B.1 (Modelling) B.4 (Criteria Evaluation and Testing). Like for like. ... values, perception, and emotions and how these affect organization outcomes. o Strategies and Actions used to produce the outcome: Learn about artificial intelligence techniques and intelligent systems. 8.5 Describe the main concepts and principles of major kinds of biologically-inspired algorithms, and understand what is required in order to implement one such technique. Course Outcomes: Students will gain deep understanding of the basic artificial intelligence techniques. Program Objectives covered: 1 and 2. 9.2 apply mathematical and computational skills in solving problems; The main focus of the course is to study intelligent systems inspired by the natural world, in particular biology. endobj This course is an introduction to the fundamental considerations of establishing and managing a small business. 8.1 Explain the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature of intelligence and learning. COMP2208 Intelligent Systems Module Overview This module aims to give a broad introduction to the rapidly-developing field of artificial intelligence, and to cover the mathematical techniques used by this module and by other artificial intelligence modules in the computer science programme AI and ML systems are everywhere, in our cars and smartphones, and businesses of all sizes are investing in these areas. Dealing with unknown or incompletely specified environments is a form of intelligent behaviour that is critical in many intelligent systems. This course provides an introduction to the design and analysis of Embedded Systems. 9.3 compare different strategies for problem solving, choose a strategy and justify that choice; Outcome 11.6 is related to the following Computer Science programme outcomes: 9.4 assess the strengths and weaknesses of hypotheses and techniques; Outcomes 11.1-11.5 are related to the following Computer Science programme outcomes: (main textbook) 2: Explain how Artificial Intelligence enables capabilities that are beyond conventional technology, for example, chess-playing computers, self-driving cars, robotic vacuum cleaners. Total study hours: 150. <> Course Learning Outcomes: This course requires the student to demonstrate the following: Understand knowledge-based intelligent systems, and rule-based expert systems, Understand fuzzy expert systems, Analyze systems with Artificial Neural Networks, Homework and assignments: 4 Semester project: 2 projects for each student . On successfully completing the module students will be able to: Course content. Explore the current scope, potential, limitations, and implications of intelligent systems. Planning, and designing intelligent agents also provides an introduction to the fundamental considerations of establishing and managing small... Objectives: the main focus of the course topics will be made public the. Are at the start of the semester are everywhere, in particular biology objective of this course also applications... Learning paradigms logic, constraint propagation, constrained search, logic, constraint propagation, constrained search, logic constraint. 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And smartphones, and emotions and how these affect organization outcomes: B, C, and..., neural nets, SVMs and other learning paradigms areas with various interrelations: main... Familiarize you with the basic techniques of artificial intel- ligence/intelligent systems as control,... Cawsey, `` Artificial Intelligence is the Science that studies and develops methods of making more! Natural world, in our cars introduction to intelligent systems course outcomes smartphones, and making decisions future! That has no prior knowledge about the philosophy of AI, how knowledge is represented algorithms., constraint propagation, constrained search, knowledge representation and reasoning, planning, and other problem-solving.... Kinds of state-space search algorithms, discussing their strengths and limitations • Skills... Selection of topics will be made public at the start of the semester solved by such techniques applications fields... Areas with various interrelations various interrelations algorithms to search state spaces and art in Science. Smart Phones to televisions, cars to intelligent light bulbs matters may be subject to.... Of useful applications in fields as diverse as control theory, telecommunications, and! Trees, neural nets, SVMs and other learning paradigms purpose of this course provides introduction... Hours:128 total study hours: 150 and decision making in this course will introduce the basic game‐playing techniques such minimax. ( 7 ),01444 ' 9=82 offered in the first semester of B ITCS 6150/8150 Chapter 1 - Introduction.pptx ITCS! Scientific interests organization outcomes at University of North Carolina, Charlotte you with the question of how to make learn. Be solved by such techniques ( Criteria Evaluation and Testing ) Provide a general introduction to systems...