Instructor Led Online | Video Conference Addis Ababa
Addis Ababa Ethiopia
Video Conference Details
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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
Training Dates
December 18, 20, 27, Jan 1, 3, 8, 10, 15, 17
Times: Every Mon & Wed 7:00 PM – 9:00 PM (Pacific Standard Time)
Each session will be recorded and the recordings will be shared after each session with students
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.
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