Fundamentals of Artificial IntelligenceLaajuus (5 ECTS)

Objective

Artificial intelligence (AI) has numerous applications in today’s world. AI has enabled us to build machines that can think rationally and sometimes as a human. AI can be seen everywhere, from self-driving cars to a simple chatbot or even YouTube video recommender. This course is a gentle introduction to AI’s basic concepts and methodologies from both theoretical and practical perspectives. The course covers essential intuitions behind different AI methods (e.g., machine learning and deep learning) as well as the business-side topics, like the implications of AI and its effect on today’s industry. By the end of this course, the student will be familiar with modern AI aspects as his/her very first steps on the journey to AI. The student will learn to think outside the box using AI and present AI-based solutions using appropriate methods discussed in the course.

Content

1. Introduction:
What is AI? – Intelligent Agents – Thinking Humanly – Acting Humanly – Thinking and Acting Rationally – Chinese Room Argument – Strong AI vs. Weak AI – History of AI

2. Modern AI:
Role of Data Science in Modern AI – What is Data? – Classical AI vs. Machine Learning – Machine Learning vs. Deep Learning – Some Applications of AI

3. Machine Learning:
General Concepts – Supervised Learning – Unsupervised Learning – Reinforcement Learning – Neural Networks – Deep Learning – Crucial Hints for Model Training – Machine Learning and Related Fields

4. AI and Machine Learning in Industry:
AI as an Indispensable Part of Today’s Industry – Some Machine Learning Use Cases in Industry – Pipeline of a Machine Learning Project – A Closer Look at an AI-based Product – Different Roles in an AI Team – Strategies to Incorporate AI in Your Company – AI Team Requirements in Your Company

5. Problems of AI:
Bias in AI – AI and Privacy – Adversarial Attacks on AI – Impact of AI on Jobs

6. Our Future with AI:
What Can be Expected from the Future of AI? – Will Machines Obtain Consciousness? – Advice for a Better Future with AI

7. Final Tasks:
Project – Self-study Essay

Assessment criteria, satisfactory (1)

- The student knows the fundamental definitions and concepts of AI.
- The student is familiar with primitive applications of AI.
- The student is familiar with the history of AI.
- The student understands the concept of data for modern AI.
- The student knows the difference between classical AI and modern AI (e.g., Machine Learning).
- The student knows the basic definition of Machine Learning and Deep Learning.

Assessment criteria, good (3)

- The student knows different types of Machine Learning algorithms.
- The student knows is familiar with neural networks.
- The student knows the related fields to AI, such as Computer Vision, Speech Processing, and NLP.
- The student understands the influential role of AI in today’s industry.
- The student is familiar with some industrial applications of AI and Machine Learning.
- The student knows the pipeline of a Machine Learning project.

Assessment criteria, excellent (5)

- The student is familiar with the connection of different components of an AI-based product.
- The student knows the different job positions related to Data Analysis, AI, and Machine Learning.
- The student knows the methods to incorporate AI in a business.
- The student knows the requirements of an AI team in a company.
- The student knows the concept of bias in AI.
- The student knows the impact of AI on jobs and public privacy.
- The student is familiar with the adversarial attacks on AI.
- The student is aware of realistic expectations for the future of AI.
- The student knows how to continue his/her journey in the field of AI after this course.

Further information

Course is only for Diploma in Machine & Deep Learning students.