- Sat, Mar 3, 2018 08:30 PM
Latitude: 25.033, Longitude: 121.565
Features & Benefits 8 sessions, each session of 2 hours spread over 4 weeks 16 hours of LIVE Instruction spread over 4 weeks Training material with lab exercises provided Each session is recorded and recordings are provided to students over Microsoft Cloud Next class starting: March 3, 2018 Course dates: March 3 - March 25, 2018 Weekly Schedule Saturday and Sunday, every week 9:30 AM - 11:30 AM (US Pacific Standard Time) each day Please confirm your local time Video Conference Link Will be sent upon registration and payment Training Provider: Omni212 Omni212 IT Training https://www.omni212.com/services/training/ Artificial Intelligence Training Course Overview In this course you will learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. About this course What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real world problems being solved with applications of intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. What you will learn in this course? In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination. Learn the fundamentals of Artificial Intelligence (AI), and apply them. What are the pre-requisites? Linear Algebra, Probability and Statistics, Data Structures & Algorithms, Truth, deduction, and Computation, Database Systems, Logic Programming. Course Outline Fundamentals of AI Statistics, Uncertainty, and Bayes networks. Principles and programming techniques of artificial intelligence - symbol manipulation, knowledge representation, logical and probabilistic reasoning, learning, language understanding, vision, expert systems Principal ideas and developments in artificial intelligence - Problem solving and search, game playing, knowledge representation and reasoning, uncertainty, machine learning, natural language processing Machine learning. Logic and planning. Applications of AI Image processing and computer vision. Natural language processing and information retrieval. Data aspect of AI, classification, clustering, normalization Intelligent agents, uninformed search Distance metrics (result set comparisons), grouping the results (K-means) Heuristic search, A* algorithm Adversarial search, games Constraint Satisfaction Problems Trained algorithms, e.g. random walk, hill climbing Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning Machine learning libraries in Python Markov decision processes and reinforcement learning Logical Agent, propositional logic and first order logic AI applications (NLP) NLP libraries, e.g. nltk AI applications (Vision/Robotics) Expert Systems Review and Conclusion Refund Policy 1. There are no refunds.2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212.