CST 3170 CST 3170 Home Page Developing Artificial Intelligence Outline Course Work 1 Course Work 2 Exam Resits: the resit coursework (for summer 2020) is the same as above. The deadline for both is the University deadline. Sessions Lecture: W 1:00-2:00 virtual in Kaltura Lab: W 2:00-4:00 Lab: W 4:30-6:30 virtual in Kaltura Pandemic Modifictions and Stuff for the Pandemic Introduction Index of Pages for the Modifictions Lectures Lecture 1:Introduction and Java Lecture 2:Learning, Classification and Lines Lecture 3:Categorisation and Cell Assemblies Lecture 4:Search and Search Spaces Lecture 5:Genetic Algorithms Lecture 6:Case Based Reasoning Lecture 7:Logic Lecture 8:Self Organising Maps Lecture 9: Semantic Nets Lecture 10:Planning Lecture 11:Multi-Layer Perceptrons Lecture 12:Adversarial Search Lecture 13:Constraint Satisfaction Lecture 14:Natural Language Processing Lecture 15:Processing with Neurons Lecture 16:Vision Lecture 17:Utility Lecture 18:SVMs Lecture 19:Deep Belief Nets Lecture 20:XML and Ontologies Lecture 21:Large Data Sets Lecture 22:Rule Based Systems Lecture 23:Robotics Lecture 24:Conclusion Labs Lab 1: Hello World and 2D Arrays, Old Lab 1 CANT Neural Net. Lab 2: Linear Categoriser. Ian developed a solution in two parts. Lab 3: Categorising with CANT Lab 4: State Space Search Lab 5: Genetic Algorithms Lab 6: Case Based Reasoning Lab 7: Prolog Lab 8: Self Organising Maps Lab 9: Coursework Support Lab 10: Coursework Support Lab 11: Coursework Support Lab 12: Multi Layer Perceptron Lab 13: Eight Queens Lab 14: Chatbot Lab 15: Processing with Neurons Lab 16: Simple Vision Lab 17: Utility Lab 18: Coursework Support Lab 19: Coursework Support Lab 20: XML Lab 21: Parallel Sort Lab Lab 22: Rule Based Systems Lab Lab 23: Search and Search Spaces Lab 24: Exam Review The handbook Exams Page This page was developed by Chris Huyck Page last modified
