Course Objectives : Pre-Requisites : General Course Contents / Syllabus : Course Learning Outcomes : Course Curriculam Course Code: CSE401 Credit Units L T P/S SW AS/DS FW No. of PSDA Total Credit Unit 3 0 2 2 0 0 0 5 Course Level UG Course Title Artificial Intelligence Course Description : SN Objectives 1 To develop semantic-based and context-aware systems to acquire, organize process, share and use the knowledge embedded in multimedia content. Research will aim to maximize automation of the complete knowledge lifecycle and achieve semantic interoperability between Web resources and services. The field of Robotics is a multi-disciplinary as robots are amazingly complex system comprising mechanical, electrical, electronic H/W and S/W and issues germane to all these. SN. Course Code Course Name SN. Module Descriptors / Topics Weightage 1 Scope of AI & Problem Solving Introduction to Artificial Intelligence. • Applications- Games, Theorem proving, Natural language processing, Vision and speech processing, Robotics, Expert systems. • AI techniques- search knowledge, Abstraction • State space search, Production systems • Search space control: depth-first, breadth-first search. Heuristic search - Hill climbing, best-first search, branch and bound. Problem Reduction, Constraint Satisfaction End, Means-End Analysis 20.00 2 Knowledge Representation Knowledge Representation issues • first order predicate calculus • Horn Clauses • Resolution, • Semantic Nets, Frames • Partitioned Nets • Procedural Vs Declarative knowledge • Forward Vs Backward Reasoning 20.00 3 Understanding Natural Languages Introduction to NLP • Basics of Syntactic Processing, • Basics of Semantic Analysis • Basics of Parsing techniques • context free and transformational grammars • transition nets • augmented transition nets • Conceptual Dependency • Scripts • Basics of grammar free analyzers • Basics of sentence generation and translation. 20.00 4 Expert System and Learning Expert System: Need • Justification for expert systems • knowledge acquisition • Case studies: MYCIN, RI. • Learning: Concept of learning • learning automation • Learning by inductions, Handling Uncertainties: Non monotonic reasoning • Probabilistic reasoning • Use of certainty factors • Fuzzy logic 20.00 5 Introduction to Robotics Robotics – Introduction , Architecture • Robot Kinematics: Position Analysis • Trajectory Planning • Sensors and vision system • Application of Robotics • Features of Robotics 20.00 SN. Course Learning Outcomes 1 Graduates will have an ability to analyze a problem, and identify and define the computing requirements appropriate to its solution. 2 Graduates will have an ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs 3 Graduates will have an ability to analyze the local and global impact of computing on individuals, organizations, and society 4 Graduate will have an ability to use current techniques, skills, and tools necessary for computing practice Pedagogy for Course Delivery : Theory /VAC / Architecture Assessment (L,T & Self Work): 80.00 Max : 100 Attendance+CE+EE : 5+35+60 Lab/ Practical/ Studio/Arch. Studio/ Field Work Assessment : 20.00 Max : 100 Attendance+CE+EE : 5+35+60 Lab/ Practical details, if applicable : 5 Graduates will have an ability to apply design and development principles in the construction of software systems of varying complexity SN. Pedagogy Methods 1 The class will be taught using remote teaching methodology. Students’ learning and assessment will be on the basis of four quadrants and flipped class method. E-content will be also provided to the students for better learning. Leaning will be theory, practical and case based method. In addition to assigning the case studies, the course instructor will spend considerable time in transforming theoretical concepts in practical oriented approach. The instructor will cover the ways think innovative SN. Type Component Name Marks 1 Attendance 5.00 2 End Term Examination (OMR) 60.00 3 Internal CLASS TEST 15.00 4 Internal CLASS QUIZ 10.00 5 Internal HOME ASSIGNMENT 4.00 6 Internal VIVA VOCE 3.00 7 Internal GROUP DISCUSSION 3.00 SN. Type Component Name Marks 1 Attendance 5.00 2 External PRACTICAL 30.00 3 External VIVA VOCE 30.00 4 Internal PERFORMANCE 15.00 5 Internal VIVA VOCE 10.00 6 Internal PRACTICAL / LAB RECORDS 10.00 SN. Lab / Practical Details 1 Write a program to implement A* algorithm in python 2 Write a program to implement Single Player Game 3 Write a program to implement Tic-Tac-Toe game problem 4 Implement Brute force solution to the Knapsack problem in Python 5 Implement Graph coloring problem using python 6 Write a program to implement BFS for water jug problem using Python 7 Write a program to implement DFS using Python List of Professional skill development activities : Text & References : 8 Tokenization of word and Sentences with the help of NLTK package 9 Design an XOR truth table using Python 10 Study of SCIKIT fuzzy No.of PSDA : 3 SN. PSDA Point 1 Group Presentation 2 Quiz 3 Case Study SN. Type Title/Name Description ISBN/ URL 1 Book • E. Rich and K. Knight, “Artificial intelligence”, 9780071008945 2 Book • N.J. Nilsson, “Principles of AI”, Narosa Publ. House 3540404554 3 Book • John J. Craig, “Introduction to Robotics”, Addison Wesley publication 978-0201103267 4 Book D.W. Patterson, “Introduction to AI and Expert Systems 978-0134771007