COURSE DESCRIPTION: ARTIFICIAL INTELLIGENCE INTRODUCTION, ETHICS & FINANCE
SYLLABUS AND LEARNING OBJECTIVES
The material of the massive open online course "Artificial Intelligence Introduction, Ethics & Finance" (https://42.cx), offered by AI-42 Market Intelligence Ltd., consists of text and interactive elements. The material is divided in eight chapters which are:
1. What is AI?
2. AI problem solving
3. Real world AI
4. Machine learning
5. Neural networks
7. Ethics of AI
8. AI & Finance
After successfully completing the course the student will be able to:
Identify autonomy and adaptivity as key concepts of AI
Distinguish between realistic and unrealistic AI (science fiction vs. real life)
Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
Formulate a real-world problem as a search problem
Formulate a simple game (such as tic-tac-toe) as a game tree
Use the minimax principle to find optimal moves in a limited-size game tree
Express probabilities in terms of natural frequencies
Apply the Bayes rule to infer risks in simple scenarios
Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
Explain why machine learning techniques are used
Distinguish between unsupervised and supervised machine learning scenarios
Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
Explain what a neural network is and where they are being successfully used
Understand the technical methods that underpin neural networks
Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work
Assessment is based on exercises, including multiple choice quizzes, numerical exercises, and questions that require
a written answer. The multiple choice and numerical exercises are automatically checked, and the exercises with
written answers are reviewed by other students (peer grading) and in some cases by the instructors.
Successful completion of the course requires at least 90% completed exercises and minimum 50% correctness. The
course is graded as pass/fail (no numerical grades).
TIME REQUIREMENT AND STUDY CREDITS
The estimated time requirement is about 60–80 hours depending on the background of the student.
As a proof of completion, each student is given an electronic certificate.
(C)2019 by AI-42 Market Intelligence Ltd.