This Winter School course, tailored for beginners from diverse fields, simplifies the complexities of artificial intelligence (AI) and machine learning (ML) through engaging, hands-on experiences and real-world applications. With a special focus on Natural Language Processing (NLP), you will gain practical skills and insights to navigate the world of AI on a deeper level.
You will explore the fundamental principles of AI and ML, learn about data preprocessing techniques, and understand the architecture and functionality of large language models (LLMs) like BERT and GPT. During the course, you will also engage in practical activities, such as building sentiment analysis models, fine-tuning pre-trained LLMs, and deploying them in various applications, including chatbots and text generation tools.
The course will also cover the ethical considerations and challenges associated with LLMs, providing a comprehensive understanding of these powerful models' potential and limitations.
This workshop-based course is ideal if you are looking to gain practical knowledge and experience in AI and ML, with a particular emphasis on the transformative capabilities of large language models. By the end of the course, you will have a strong foundation in AI and machine learning. You will also be equipped with the skills to apply large language models to solve real-world challenges and develop innovative solutions within your respective fields.
Dariush Salami is teaching the brand new summer course Intro to AI and Machine Learning (ML) by Aalto University Summer School, and hopes that students will leave the course with a sense of excitement and curiosity about the potential of AI and ML – and a clear understanding of how they can contribute to the field.
Arjun Krisha, a construction engineering student from India, attended the Introduction to AI and Machine Learning summer course by Aalto University Summer School.
18 – 29 January 2027: Lecture weeks on-site at Aalto University campus
| WEEK 1 | Monday 18 January | Tuesday 19 January | Wednesday 20 January | Thursday 21 January | Friday 22 January |
|---|---|---|---|---|---|
| Morning (9-12) | Introduction to AI, ML, and LLMs |
Basics of Machine Learning​ |
Data Preprocessing for NLP​ |
Introduction to LLMs and Their Applications​ |
Supervised Learning for NLP Tasks​ |
| Afternoon (13-16) |
| WEEK 2 | Monday 25 January | Tuesday 26 January | Wednesday 27 January | Thursday 28 January | Friday 29 January |
|---|---|---|---|---|---|
| Morning (9-12) |
Advanced NLP Techniques​ |
Language Generation with LLMs​ |
Ethical Considerations and Challenges in LLMs​ |
Model Deployment and Integration in Applications​ |
Final Project and Presentation​ |
| Afternoon (13-16) |
*Note that this is a preliminary schedule subject to change or updates.
Alongside the lectures, students have independent work, either in groups or individually. Students can coordinate with their teammates to complete these assignments outside of teaching hours.
The total workload of the course includes both contact lectures and the estimated amount of independent work. Some of the courses also have pre- or post-assignments included in the independent working hours.
-> Find the total course workload below
This course has no strict prerequisites, though basic coding skills are an advantage for getting the most out of it. It's ideal for a diverse range of participants: students, professionals, and enthusiasts alike who want to deepen their understanding of artificial intelligence and machine learning, with a particular focus on Large Language Models.
Familiarity with coding, even at a very basic level, is encouraged.
Professionals in fields such as engineering, data science, software development, and business will find particular value in the course, as it offers practical skills for applying AI techniques to real-world problems. The workshop also helps participants build the tools they need to keep pace with the fast-evolving AI landscape.
Recommended reading
To get the most out of the course, participants may find it helpful to review the following texts beforehand:
Recommended background knowledge
Some familiarity with the following tools and frameworks is beneficial:
Recommended math skills
A working knowledge of the following mathematical concepts will help participants engage more fully with the material:
Eligibility requirements
Participants must have completed a high school or vocational degree or equivalent by the time the course starts. However, they do not have to be a degree student at a university to participate in our courses. Additionally, all summer and winter school course students must be 18 years or older, as this is the legal age in Finland.
(Updated 30 January 2026)
Understand core concepts
Preprocess text data
Apply machine learning to NLP and develop NLP applications
Analyse and mitigate ethical concerns
Deploy and integrate models
Engage in hands-on projects
The working and teaching methods are interactive and engaging, combining various instructional strategies.
Course workload
Lectures and practical sessions 30h
Independent study 21h
Project work and assignments 30h
Total workload 81h (3 ECTS)
The course is graded as pass/fail.
Please note that the undergraduate courses offered by Aalto University Summer School can not be included in Aalto degree studies.
Please include a motivation letter or video in your application. In your motivation letter or video, describe your motivation for taking this course. The recommended length for the motivation letter is 150-250 words, and 1-2 minutes for the video.
In your motivation letter or video, please answer these questions:
Tuition fees for Winter School courses
One course: 1270€ (incl. VAT 25,5%)
Social program fee
If you need a visa, we recommend applying by October 15th 2026.
Learn about the Aalto University Summer School cancellation terms. You will receive the full terms before making the payment and confirming the course participation.
Doctor of Science (SDr.) Dariush Salami is a distinguished Radio Research Scientist at Nokia Bell Labs. He has deep expertise in AI and machine learning for wireless networks, including cutting-edge 5G and 6G technologies. He earned his PhD from Aalto University as a Marie Skłodowska Curie fellow, focusing on human-centric sensing using mmWave radars.
Salami has extensive industrial experience, notably serving as the CTO of Rectified.ai, a platform-agnostic automatic machine-learning solution. He has lectured the Machine Learning D course for nearly 1000 students, making it the largest AI/ML course in Finland.
His innovative work is recognised through several patents and numerous publications in prestigious journals. Additionally, he has secured multiple grants, including from the Nokia Foundation and Google, underscoring his contributions to advancing technology.
The application period for Winter School 2027 runs from 15 September to 15 November 2026.
Everything you need to know about Summer School! Make this summer unforgettable and experience the best of Aalto University and Finland under the Nordic summer sun.
Get to know the application guidelines and tuition fees.
