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Explico , Instructor - Introduction to AI


Explico is a management consultancy that leverages depth and breadth of business and financial expertise to define, analyze, and solve business needs. They have the ability to solve business needs no matter how complex. Explico's approach draws on business and financial concepts, academic knowledge, and real-world business experience.

Instructor: Explico

Artificial Intelligence Learning Approaches, Tools, Problems, and Applications.

Understand the basics and be conversant on topics in Artificial Intelligence, and identify further areas of interest in AI.

Course Description

In this course you will learn about Artificial Intelligence including its History, Problem Solving, Learning Approaches, Tools, Problems to be Solved and Applications.

What am I going to get from this course?

Understand the basics and be conversant on topics in Artificial Intelligence, and identify further areas of interest in AI.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

There is no prerequisite knowledge.

Who should take this course? Who should not?

Students, professionals who want to pursue their career in AI and learn the basics of Artificial Intelligence.


Module 1: Introduction to AI

Lecture 1 Course Introduction

Introduction to Artificial Intelligence.

Lecture 2 Introduction

Introduction to Artificial Intelligence.

Lecture 3 History
Lecture 4 Learning Approaches

Approaches to learning including: Logic and Rule, Computational with statistic, & Data, Symbolic, and Sub-symbolic.

Lecture 5 AI Methods

Artificial Intelligence Applications in Problem Solving, Knowledge and Reasoning including with Certainty and Uncertainty Learnings.

Lecture 6 Problems Solving

A basic framework of problem solving.

Lecture 7 Problems to be Solved
Lecture 8 Machine Learning

Machine learning methods based on data Models and Statistical learning approaches including Supervised and Unsupervised learning types.

Lecture 9 Deep Learning

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.

Lecture 10 AI Applications

Applications including robotics, expert system, self-driving cars, neural language processing, Alexa, Google Translate.

Lecture 11 Conclusion

Course summary.