SummerAI

Study materials and assignment instructions.

Schedule: TBD

Lecturer: Priit Järv

Lectures

• week 1: Introduction
• week 2: Machine Learning
• week 3: Large Language Models
• week 4: Technical Aspects of Machine Learning
• week 5: Ethics and Impact of AI
• week 6: Closed book test

Assignments

Homework

Complete these as a group, outside the classroom.

Teachable Machine (deadline XX.YY)

Lab Work

Complete these as a group.

For each assigment, write a text file with answers to the questions or results of the lab work. One or more groups will present their results to everyone. There will be a joint discussion.

• Classical Machine Learning (XX.YY)
• Neural Network (XX.YY)
• Using LLMs through a program (XX.YY)

Submitting results

Where to submit will be clarified before the start of the summer school.

Grading

Your grade will be formed by:

• 50% participation: actively taking part of solving the homework. Will be self-graded.
• 50% written test.