introduction

This booklet contains part of the material of the course ICAT1100 Artificial Intelligence: Concepts, Challenges, and Opportunities for Fall semester 2021.

Learning goals

After completing the course, the student is able to:

1. Define and explain various artificial intelligence (AI) concepts, challenges, and opportunities: e.g., problem-solving, knowledge representation, machine learning, decision-making, natural language processing, and expert systems.

2. Explain fundamental algorithms and the related mathematical and programming concepts of AI,

3. List applications of AI in various fields,

4. Use AI software or tools for specific purposes, and

5. Recognize ethical problems related to AI and to suggest constructive solutions to those.

Content

What is AI, manifestation of AI, algorithmic thinking, searching techniques for problem solving, searching strategies, propositional logic: representation and inference, probability theory and Bayesian concepts, concepts and review of machine learning algorithms, game theory and decision making, applications of AI in Computer vision and robotics, NLP workflow: text mining, word embedding, skip-grams, cbow, programming tools: Jupyter, basic programming structures. basic data structures and knowledge representation, Keras and applications, Scikit learn, Orange data management tool, ethical and societal challenges, broad collection of ethical challenges, categories of ethical challenges of AI

Implementation

lectures: 40 hours, students working in groups for AI project development throughout the course. There will be quizzes.

Material

1. Instructors notes

2. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. (3rd Edition), Pearson 2009. [8]

3. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep learning, MIT press 2016 [4]

Assesment

0-5