Artificial Intelligence

Artificial intelligence (AI) is a research area that studies how intelligent human behavior can be realized on a computer. The ultimate goal of AI is to develop a computer that can learn, plan, and solve problems on its own.

Although AI has been researched for more than half a century, we still cannot build a computer that is as intelligent as a human in all aspects. However, we have many successful applications. In some cases, the computer equipped with AI technology can be even smarter than us.

The main research topics in AI include: problem solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning and so on.

These questions are of course closely related. For example, the knowledge acquired through learning can be used for both problem solving and reasoning. Indeed, problem-solving skills should be acquired through independent learning.

In addition, problem-solving methods are useful for both reasoning and planning. In addition, both natural language understanding and machine vision can be solved using methods developed in the field of pattern recognition.

In this course we will learn the most basic knowledge needed to understand AI. We will introduce some basic search algorithms for problem solving; Knowledge representation and reasoning; Pattern recognition; fuzzy logic; and neural networks.

Course Outline:

• Unit1: AI System Introduction
• Describing the eras of computing 
• Explaining the difference between deterministic and probabilistic systems 
•  Describing the types of AI 
•  Explaining what the main focus of AI is 
•  Listing oc practical applications of AI
•  Explaining what cognitive computing is 
• Describing the characteristics of cognitive systems 
• Explaining the landscape of cognitive computing in the industry 
• AI Future trends
• Unit2: Introduction to Machine Learning 
• Explaining what machine learning is 
• Describing the types of machine learning 
• Explaining what neural networks are and why they are important in today’s AI’s field 
•  Explaining what domain adaptation is and its applications 
•  Explaining what NLP is 
• Describing different NLP processes 
• Listing tools and services for NLP
•  Identifying NLP use cases
•  Learn how to build your own language translator with an AI guide.
• Defining CV
•  Knowing the history of CV and its advancement with AI
•  Listing tools and services for CV 
• Identifying CV use cases 
•  Analyze, Classify and detect objects using IBM Watson visual recognition service. 
• Explaining what cognitive computing is 
•  Describing the characteristics of cognitive systems 
•  Explaining the landscape of cognitive computing in the industry. 
• Classifying images using Node Red Guide
• Unit3: Artificial Learning Foundation
• Explaining what IBM Watson is and how it works 
•  Explaining how Watson technology is made available to developers and organizations
•  Describing how Watson technology is being applied to solve real world problems 
• Recognizing the Watson services available today on the IBM Cloud 
• Listing the Watson services 
• Explaining the capabilities of each Watson service 
•  Describing the purpose of training the various Watson services to adapt them to a closed domain 
•  Listing the Watson services that can be trained Artificial Intelligence and Deep Learning
•  Listing the Watson services that cannot be trained
•  Describing what Watson Knowledge studio is 
•  Predict Fraud using AutoAI guide. 
•  Working with jupyter notebooks, collaborators using Watson Studio. 
• Understanding refining of data. 
•  Visualization on refined data. 
•  Listing the Watson services that can be trained with Watson Knowledge Studio.
•  Creating Machine Learning Model with Knowledge studio 
•  Using Watson API Explorer to interact with the Watson services REST API, to test your call to the API, and to view live responses from the server.
• Unit4: Artificial Intelligence Analyst
• Describing different NLP processes 
• Listing tools and services for NLP