Artificial Intelligence Lecture 1: Introduction مقدمة باكسرين

مساق: الذكاء الاصطناعي المحاضر: د. معتز خالد سعد كلية: تكنولوجيا المعلومات Multimedia Programming II Course Outline وصف المساق Artificial Intelligence Course Outline Instructor: Motaz Saad Course Name: Artificial Intelligence / Intelligent and Decision Support Systems Course ID: CSCI4304 / SICT4402 Term: Spring 2020 Prerequisites: Programming, Data Structure. Course Description This course provides students with the main fundamentals of Artificial Intelligence (AI). The course covers the main techniques that are used in AI examples (from chess-playing to self-driving cars). These techniques include search algorithms, probability, reasoning and inference, programming logic, expert systems, rule-based systems, fuzzy logic, machine learning, knowledge representation, pattern recognition, and natural language processing. The course helps students to use AI to solve specific problems in their future careers. The theoretical part of the course focuses on understanding concepts, structures, and algorithms, while the practical part (lab) includes a set of exercises to be performed using AI tools such as CLIPS, Weka, and Matlab. Textbooks Michael Negnevitsky, Artificial Intelligence: Intelligent Systems Approach, 3/E, ISBN: 9781408225745, 2011. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition 3/E, ISBN: 9781292153964, 2017. Topics Introduction: What is AI? State of the art of AI Intelligent Agents (1 week, chapter 2 from Modern Approach book) Problem Solving and Search Algorithms (2 weeks, chapter 03 and chapter04 from Modern Approach book) Problem Solving Search Algorithms Breadth-first search Uniform-cost search Depth-first search Depth-limited search Iterative deepening search Best-first search A* search Heuristics Game Playing (1 week, chapter06 from Modern Approach book). MinMax Algorithm. Rule-based expert systems (1 week, Chapter 02 from Intelligent Systems Approach book) Fuzzy expert systems (1 week, Chapter 04 and Chapter 05 from Intelligent Systems Approach book) Artificial neural networks (Supervised) (Chapter 07 – Artificial Neural Networks – Supervised Learning) Artificial neural networks (Unsupervised) (Chapter 08 – Artificial Neural Networks – Unsupervised Learning) Evolutionary computation (Chapter 09 – Evolutionary Computation – Genetic Algorithms) Hybrid intelligent systems Chapter 11 – Hybrid Intelligent Systems – Neural Expert Systems and Neuro-fuzzy Systems Chapter 12 – Hybrid Intelligent Systems – Evolutionary Neural Networks and Fuzzy Evolutionary Systems Natural Language Processing (NLP Intro) /watch/Ui_kUvE_v_l_k Video Lectures IUG Video Lectures My Desktop Recordings Grading Activities and Assignments 20% Mid exam 30% Final Exam 50% Tools CLIPS Download User Guide Tutorial Search tools from AI Space PyKnow: Expert Systems for Python. Docs. WEKA: a collection of machine learning algorithms Genetic Algorithms with Python: Tutorial, code Neural networks with python build Neural Network from scratch in Python Python Libraries TensorFlow Blocks Lasagne Keras Deepy Nolearn NeuPy Matlab toolboxes: Genetic Algorithm, Fuzzy Logic Applications Image captioning, image capturing with deep learning ** كافة الحقوق محفوظة لصالح الجامعة الإسلامية بغزة | https://www.iugaza.edu.ps

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