introduction to intelligent systems course outcomes

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. View Chapter 1 - Introduction.pptx from ITCS 6150 at University of North Carolina, Charlotte. 8. • Intellectual Skills: B.4 (Criteria Evaluation and Testing). This course provides an introduction to the design and analysis of Embedded Systems. 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. The main focus of the course is to study intelligent systems inspired by the natural world, in particular biology. A. Cawsey, "The Essence of Artificial Intelligence", Prentice-Hall, 1998. Intelligent Systems - ITCS 6150/8150 Chapter 1 Artificial Intelligence Dr. Dewan Tanvir Ahmed Department Bio-inspired intelligent systems have thousands of useful applications in fields as diverse as control theory, telecommunications, music and art. 9 0 obj <> Intelligent Transportation Systems (ITS) represent a major transition in transportation on many dimensions. 9.4 assess the strengths and weaknesses of hypotheses and techniques; Like for like. 8.2 Describe the main kinds of state-space search algorithms, discussing their strengths and limitations. endobj %���� Outcome 11.6 is related to the following Computer Science programme outcomes: 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 Course Outcomes: Students will gain deep understanding of the basic artificial intelligence techniques. ... learn about how intelligent systems use uncertainty in reasoning and decision making in this free online course. <> • Intellectual Skills: B.1 (Modelling) B.4 (Criteria Evaluation and Testing). endstream <> 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. Introduction to Intelligence. 7 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. ISE is a set of modern Systems Engineering areas with various interrelations. <>>> 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. L1, L2 See general guidelines for examination at the MN Faculty autumn 2020. endobj In addition, it covers applications of decision trees, neural nets, SVMs and other learning paradigms. Learn about how artificial intelligence is used to tackle complex real world problems like speech recognition and machine translations using machine techniques. 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. purpose of this course is to familiarize you with the basic techniques of artificial intel- ligence/intelligent systems. Artificial intelligence is the science that studies and develops methods of making computers more /intelligent/. Defining Intelligence. • Intellectual Skills: B.1 (Modelling) B.4 (Criteria Evaluation and Testing). COMP5200: Further Object-Oriented Programming. 5 0 obj endobj 2: Explain how Artificial Intelligence enables capabilities that are beyond conventional technology, for example, chess-playing computers, self-driving cars, robotic vacuum cleaners. 13.1 Main assessment methods Unit Learning Outcomes (ULO) Students who successfully complete this unit will be able to: 1. <> <> Explore the current scope, potential, limitations, and implications of intelligent systems. 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). Outcomes 12.3 and 12.4 are related to the following Computer Science programme outcomes: This course considers ITS as a lens through which one can view many transportation and societal issues. Course Objectives: The main objective of this course is to : Provide a general introduction to intelligent systems . Academic Honesty: Cheating in this course will not be tolerated. stream • Knowledge and Understanding of: A.2 (Software), A.4 (Practice) and A.5 (Theory). endobj 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. This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. 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. 'Mathematics for Intelligent System 1' is a course offered in the first semester of B. ABET Criteria covered: B, C, G and I. endobj stream 6 0 obj Homework and assignments: 4 Semester project: 2 projects for each student . The course also aims to give an overview of the historical, philosophical, and logical foundations of AI.

Social Sciences Undergraduate Stage 2 & 3. 8 0 obj 2 0 obj endobj 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. A2 – Practical assignement (25%) This course provides a broad introduction and details of faculty research areas. • 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). <> A selection of topics will be made public at the start of the semester. 8.1 Explain the motivation for designing intelligent machines, their implications and associated philosophical issues, such as the nature of intelligence and learning. S. Pinker. This course gives a basic introduction to machine learning (ML) and artificial intelligence … Outcome 12.5 is related to the following Computer Science programme outcomes: Course content. <> <> The intended generic learning outcomes. • Knowledge and Understanding of: A.2 (Software), A.4 (Practice) and A.5 (Theory). 4 0 obj On successfully completing the module students will be able to: Course Description. 13 0 obj The course topics will vary each year, dependent on available teachers and scientific interests. Total study hours: 150. endobj Artificial intelligence (AI) and machine learning (ML) are about creating intelligent systems – systems that perceive and respond to the world around them. Outcomes 12.1-12.2 are related to the following Computer Science programme outcomes: Over the last century or so, intelligence has been defined in many different ways. The intended subject specific learning outcomes. Please read our full disclaimer. $.' %PDF-1.5 This course will introduce the basic game‐playing techniques such as minimax search and alpha‐beta pruning. 3 0 obj Embedded Systems are at the heart of almost all modern technologies; Smart Phones to televisions, cars to intelligent light bulbs. Course description. 9.2 apply mathematical and computational skills in solving problems; 12 0 obj Several algorithms and methods are discussed, including evolutionary algorithms. ... values, perception, and emotions and how these affect organization outcomes. 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. Programming assignments are an integral part of the course. 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). "Digital Biology", Simon & Schuster, 2002, See the library reading list for this module (Canterbury). 9.5 use the library and appropriate internet resources in support of learning. <>/XObject<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/MediaBox[ 0 0 960 540] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> <> For example, consider a robot in maze that has no prior knowledge about the maze layout. • 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 A1 – Practical assignement (25%) Outcome 12.5 is related to the following Computer Science programme outcomes: Dealing with unknown or incompletely specified environments is a form of intelligent behaviour that is critical in many intelligent systems. Finds a policy that reaches (4,3) via (2,1), (3,1), (3,2), (3,3) Suboptimal policy 9.3 compare different strategies for problem solving, choose a strategy and justify that choice; Robotics and Intelligent Systems, MAE 345, provides students with a working knowledge of methods for design and analysis of robotic and intelligent systems. 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, o Strategies and Actions used to produce the outcome: Learn about artificial intelligence techniques and intelligent systems. This course introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. 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. Apply different AI/IA algorithms to … ... Research has found “g” to be highly correlated with many important social outcomes and is the single best predictor of successful job performance. 13.2 Reassessment methods Particular attention is given to modeling dynamic systems, measuring and controlling their behavior, and making decisions about future courses of action. 2 hour unseen written examination (50%) 11 0 obj 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. On successfully completing the module students will be able to: • Transferable Skills: D.3 (Information Technology) and D.5 (self-management). endobj Program Objectives covered: 1 and 2. This course is an introduction to the fundamental considerations of establishing and managing a small business. However, courses, services and other matters may be subject to change. The module also provides an introduction to both machine learning and biologically inspired computation. Private study hours:128 P. Bentley. 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. ... Social Media and Intelligent Systems. The focus of this course is on core AI techniques for search, knowledge representation and reasoning, planning, and designing intelligent agents. 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. “Artificial Intelligence -A Modern Approach” by S. Russell and Peter Norvig, prentice-Hall. A*, interative deepening), logic, planning, knowledge representation, machine learning, and applications from areas such as computer vision, robotics, natural language processing, and expert systems. 2. S.J. 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 Machine learning is concerned with the question of how to make computers learn from experience. ",#(7),01444'9=82. This course also explores applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. Course content. Explain a range of techniques of intelligent systems across artificial intelligence (AI) and intelligent agents (IA); both from a theoretical and a practical perspective. "How the Mind Works", W.W. Norton & Company, 1999. ((CSCI-261 and MATH-251) or permission of instructor) Course Outcomes 10 0 obj • Intellectual Skills: B.4 (Criteria Evaluation and Testing). We use cookies to improve your experience on our site. AI and ML systems are everywhere, in our cars and smartphones, and businesses of all sizes are investing in these areas. endobj endobj Russell & P. Norvig, "Artificial Intelligence: a modern approach", 2nd Edition. Lectures: 45 hours/semester, 3 hours/week. Total contact hours: 22 <> <> Possible topics include: Introduction to artificial intelligence and intelligent agents Problemsolving and search methods Knowledge, reasoning, and planning (KRP) in Computer Science and Engineering (Artificial Intelligence) program … 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. Offered by IBM. 9. Course Objective: To make students understand and explore the techniques underlying the design of Intelligent Systems. Tech. Some IDEATE courses and some SCS undergraduate and graduate courses might not be allowed based on course content. 6. endobj 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. 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. Outcomes 11.1-11.5 are related to the following Computer Science programme outcomes: Course Description. Prentice-Hall, 2002. Explain what constitutes "Artificial" Intelligence and how to identify systems with Artificial Intelligence. 9.1 Discuss and give examples of the role of analogy and metaphor in science and engineering; 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. 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