Artificial Intelligence

In this course, the students will be exposed to the overview, history, why AI is needed and application areas involved. Then Fuzzy Logic will be presented where the topics covered are introduction to Fuzzy Logic, Fuzzy set and Fuzzy logic Control System and it’s latest application such as fuzzy logic in expert systems and energy conversion. Next, Neural Network (NN) will be covered with Introduction to NN or how the brain work, the neuron as a simple computing element, biological motivations: McCulloch and Pitts neuron, Hebbian learning, peceptron, multilayer neural networks, hidden layer of NN, and back propagation. Eventually students will visit Machine Learning that covers Supervised Learning, Discriminative Algorithms, and Generative learning algorithms. Several tools in Machine Learning are going to be explained as well such as Gaussian discriminant analysis, Naive Bayes, Support vector machines. unsupervised learning, clustering, K-means, mixture of Gaussians, Factor analysis, PCA (Principal components analysis), and ICA (Independent components analysis). The students are introduced to future AI namely deep learning and current fuzzy implementations for industry and domestic such as autonomous controller and manufacturing applications.

At the end of this course, the students should be able to:

• 1. Analyze the implementation of artificial Intelligence techniques such as fuzzy logic algorithm, neural network and machine learning in problem solving.

  2. Design an intelligent system to achieve predetermined specifications.

INTRODUCTION TO MACHINE LEARNING

At the end of this module, student should able to:

1. Understand the concept of artificial intelligence and machine learning.

2. Learn the application of machine learning and its categories.

*This topic is divided into SEVEN parts and each part of video is duration of 5 minutes. Kindly enjoy the video and answer the quiz.