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Understand the basics and be conversant on topics in Artificial Intelligence, and identify further areas of interest in AI.
In this course you will learn about Artificial Intelligence including its History, Problem Solving, Learning Approaches, Tools, Problems to be Solved and Applications.
Students, professionals who want to pursue their career in AI and learn the basics of Artificial Intelligence.
Introduction to Artificial Intelligence.
Approaches to learning including: Logic and Rule, Computational with statistic, & Data, Symbolic, and Sub-symbolic.
Artificial Intelligence Applications in Problem Solving, Knowledge and Reasoning including with Certainty and Uncertainty Learnings.
A basic framework of problem solving.
Machine learning methods based on data Models and Statistical learning approaches including Supervised and Unsupervised learning types.
Supervised Learning techniques including Neural Networks, Multilayer Perceptron Deep stack network, Convolutional Neural Network. Unsupervised techniques including Deep Belief Network, Boltzman Machine Restricted Boltzman Machine, Autoencoders.
Applications including robotics, expert system, self-driving cars, neural language processing, Alexa, Google Translate.
Course summary.
