Accra Artificial Intelligence | AI | IT Training | Disruptive Technologies |
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Artificial Intelligence Training
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.
- 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
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
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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