Module Catalogues, Xi'an Jiaotong-Liverpool University      Module Code: MFE204TC Module Title: Artificial Intelligence and data analysis Module Level: Level 2 Module Credits: 5.00 Academic Year: 2021/22 Semester: SEM1 Originating Department: School of Intelligent Manufacturing Ecosystem, Taicang Pre-requisites: N/A     Aims This module gives an introduction to Artificial Intelligence, by (1) describing the major knowledge representation paradigms; (2) providing a grounding in Prolog, used as a vehicle for practical illustrations; (3) considering knowledge based systems, and (4) introducing students some of the modern AI concepts, machine learning algorithms. The specific aims are: (1) To introduce students to the concept of knowledge representation, common knowledge representation paradigms and the issues involved in knowledge representation. (2) To introduce students to the sorts of systems that can be built using artificial intelligence techniques, in particular knowledge based systems. (3) To provide a grounding in Prolog. (4) To introduce students some of the modern AI developments, including vision and representation, and visual object recognition. Learning outcomes  A .Account for the principles of knowledge representation. B. Gain experience in the search techniques and logic, particularly as related to knowledge representation. C. Become familiar with the major knowledge representation paradigms: production rules, prepositional and first order predicate calculus and structured objects. D. Apply the knowledge of how these representations can be manipulated to solve problems in a knowledge based systems context. E. Acquire the essentials of Prolog so as to enable further exploration of the above in practical usages in AI. F. Acquire the fundamental knowledge of some of the modern AI concepts and technologies, including vision and representation, and visual object recognition. Method of teaching and learning  Students will be expected to attend two to three hours of formal lectures as well as to participate in one to two hours of supervised practical in a computer lab in a typical week. Lectures will introduce students to the academic content and practical skills which are the subject of the module. Computer practical will be used to introduce students to functional language and logic programming language compilers, interpreters, and supporting tools as well as techniques for the development of functional and logic programs. Computer practical will also allow students to use those tools and practice the acquired techniques. In addition, students will be expected to devote six to seven hours of unsupervised time to solving continuous assessment tasks and private study. Private study will provide time for reflection and consideration of lecture material and background reading. Continuous assessment will be used to test to what extent practical skills have been learnt, in particular, assessment tasks will be solved individually and each solution comprises the resolution, using sound software engineering techniques, of the given problems expressed in terms of a requirements statement. Syllabus  Lecture 1 Introduction to AI, historical overview of AI Lectures 2-4 Search strategies Lectures 5-8 Knowledge Representation and fundamentals of Prolog Lectures 9-10 Rule-based systems Lectures 11-13 Propositional Logic Lectures 14-16 First Order Logic for knowledge representation Lectures 17-19 Machine learning Lectures 20-23 Vision and representation Lecturers 24-26 Introduction to visual object recognition Delivery Hours   Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total Hours/Semester 26    13  13    98  150

Assessment

Sequence	Method	% of Final Mark
1	Final Exam	60.00
2	Assessment Task	20.00
3	Group Project	20.00

Module Catalogue generated from SITS	CUT-OFF: 12/16/2019 7:35:55 AM