Introduction to Artificial Intelligence

Course details

Computer scientists are just a small slice of people working in artificial intelligence (AI). Most people working with AI are just like you. They’re professionals, teachers, and students who want to use AI to enhance their products, creativity, and career. AI has been around for over half a century. Despite huge advancements in predictive and generative AI, the core concepts of artificial intelligence are still accessible.

This course is designed for project managers, product managers, directors, executives, and students starting a career in AI. First, learn what it means for a system to display “intelligence.” Then, explore the difference between classic predictive AI and newer generative AI. Next, you’ll get an overview of machine learning algorithms, artificial neural networks, foundation models, and deep learning. From the AI curious to the AI careerist, this course will help you get started with intelligent systems.

Learning objectives
Explore the differences between symbolic systems and machine learning.
Compare predictive and generative AI.
Discover the importance of different machine learning algorithms.
Understand the power of artificial neural networks, deep learning, foundation models, and generative AI.

Concepts

0. Introduction

01 - Why you need to know about artificial intelligence

1. What Is Artificial Intelligence

02 - Define general intelligence
03 - The general problem-solver
04 - Strong vs. weak AI

2. Popular Uses for Artificial Intelligence

05 - Predictive AI
06 - Generative AI

3. The Rise of Machine Learning

07 - Machine learning
08 - Artificial neural networks

4. Common AI Systems

09 - Searching for patterns in data
10 - Robotics
11 - Natural language processing
12 - The internet of things
13 - Generative systems

5. Learn from Data

14 - Labeled and unlabeled data
15 - Massive datasets
16 - Data models

6. Identify Patterns

17 - Classify data
18 - Cluster data
19 - Reinforcement learning

7. Machine Learning Algorithms

20 - Common algorithms
21 - K-nearest neighbor
22 - K-means clustering
23 - Regression
24 - Naive Bayes

8. Fit the Algorithm

25 - Select the best algorithm
26 - Follow the data
27 - Overfitting and underfitting

9. Artificial Neural Networks

28 - Build a neural network
29 - Weighing the connections
30 - The activation bias

10. Improve Accuracy

31 - Learning from mistakes
32 - Step through the network

11. The Rise of Generative AI

33 - Self-supervised learning
34 - Foundation models
35 - Large language models (LLM)
36 - Image diffusion models

12. Generative AI Architecture

37 - Generative adversarial networks (GAN)
38 - Variational autoencoder (VAE)
39 - Transformers

13. Ethical and Legal Challenges

40 - The alignment problem
41 - Decision traceability
42 - Copyright challenges
43 - Privacy concerns

14. Where to Go from Here

44 - Using AI systems
45 - Applying AI to solve problems