The course covers various methods within artificial intelligence (AI) and machine learning (ML), and their applications. Examples include algorithms for search, optimization and classification, which to a large extent consist of bio-inspired approaches.
After taking the course, you will:
have insight into novel methods (within evolutionary computation, neural networks, swarm intelligence) used in artificial intelligence (AI) and machine learning (ML)
have knowledge about how to use AI and ML for various applications
be able to search for literature outlining state-of-the-art within a specific research field.
be able to critically assess scientific papers and be familiar with the structure of a scientific paper
be able to design and conduct experiments using AI and ML, with emphasis on evaluation
have experience in presenting scientific work to others
have experience in writing a scientific paper
This lecture goes over the definition of AI and what might be considered AI. It discusses the concept of the "Turning Test" and whether or not modern day AI applications would pass it. The lecture also covers an overview of what AI is by definition and how to tell if a solution is AI based or not.
This lecture covers the concept of Agents and the different types of Intelligent Agents and how they work together. The lecture provides and overview on how to design an Agent based solution. It also covers the different types of Agent Based solution scenarios.
This lecture covers the concept of problem solving in Artificial Intelligence. It proposes a methodology for how to approach the solving problem technique and discusses how to design a solution to an Agent or Search based problem resolution.
This lecture covers how to complete the first in class assignment for the weekend AI class.
This lecture covers the concept of searching in informed and uninformed formats. Only an overview of searching as a concept is presented. The textbook goes over the implementation of the different searching algorithms that will be used in problem solving using searching methods.
This lecture covers how to complete the second in class assignment for the weekend AI class.
This lecture provides an overview of the LISP and Scheme programming languages. The lecture goes over an overview of what functional programming languages are and how they are used to solve problems. Dr. Racket installation and usage is also demonstrated.
This lecture covers how to complete the third in class assignment for the weekend AI class
This lecture covers how to complete the fourth in class assignment for the weekend AI class
This lecture overviews the concept of Genetic Algorithms and how they apply to Artificial Intelligence. It provides a framework for understanding how this type of problem solving can be applied to computer science problems.
This lecture covers how to complete the first four take-home assignment
This lecture covers how to complete the 5th and 6th in class assignment
This lecture provides an overview of how Prolog can be used to solve Artificial Intelligent logic related problems. It provides and overview of how prolog works with knowledge bases and with list processing. Its just an orientation to the Prolog concept and functionality.
This video goes over the exam
This lecture discusses the concept of Knowledge Acquisition and Discovery. It overviews the concept of Knowledge Engineering and how to create a knowledge base in terms of the logic and AI components. It also finishes the discussion on how Expert Systems and how they work. An overview of the problems with using knowledge and Truth Maintenance is also explored though many different examples. Some of my elaborations and examples may sound off target but they are demonstrating knowledge issues and the value of keeping knowledge databases and information up-to-date in the real world.
This lecture covers an overview of Bayesian Networks and how they are used in Artificial Intelligence. The concept of probability and general statistics applied to AI and network construction is also discussed. The lecture provides a high level overview without getting into the technical details about how the Bayes Nets are constructed. Its for general concept and overview purposes.
This lecture overviews the concept of Game Playing. The overall concept of designing AI applications to simulate game playing and game concepts is explored.
This lecture covers the concept of Artificial Neural Networks and the different varieties that are used with computer artificial intelligence applications and problem solving. It is just an overview of the concepts and what makes up the ANN concepts.
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Advance Certification in Artifiical Intelligence AI from Belhaven University
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