SAIL - Split AI Lab School of Machine Learning and Data Science

26 May 2025, 09:00 → 6 Jun 2025, 19:00 Europe/Zagreb

UNIST Technological Park, Croatia

Split AI Lab

Welcome to Split AI Lab

Split AI Lab is an intensive two-week school designed for students, researchers, and early-career professionals wanting to build a strong foundation in Machine Learning and Data Science. Through lectures, hands-on coding sessions, and real-world applications, participants will gain the essential knowledge and practical skills needed to confidently start their journey in AI.

Who is this school for?

This school is aimed at participants who:

• Have basic knowledge of Python programming and computer literacy.
• Understand fundamental statistics but have not had formal exposure to Machine Learning.
• Want to apply ML techniques in industry, academia, or research.

What to Expect?

• Hands-on coding sessions using Jupyter Notebooks and common ML libraries (NumPy, Pandas, Scikit-learn, etc.).
• Practical projects and Kaggle challenges to apply knowledge.
• Comprehensive training, covering statistical foundations, supervised & unsupervised learning, neural networks, and deep learning basics.
• Networking with fellow participants and industry experts.

Program Structure

The school follows a full-day schedule (9 AM - 5 PM) with structured lectures, interactive discussions, and practical coding exercises.

Registration

The registration fee of 1.310,00 Euros covers the attendance to the school, all lectures and coffee breaks, studying materials, and social events. The early bird registration fee benefits from a 5% discount. 

Location & Logistics

The school will take place in Split, Croatia, offering a unique learning experience in a beautiful Mediterranean setting. 

The working language of the school is English.

For details on logistics, deadlines, and registration, please visit the corresponding pages.

Contact

For any questions, reach out to us at contact@splitailab.com.

 

Monday 26 May

1 Welcome

2 Introduction to ML

3 Statistics I

10:45 Coffee break

4 Statistics II

12:30 Lunch break

5 Installation of nec. packages

6 Preprocessing I

7 kNN

15:30 Coffee break

8 kNN Hands-on

Tuesday 27 May

9 Metrics and Evaluation I

10:45 Coffee break

10 Bayes

12:30 Lunch break

11 Hands-on

12 Feature selection

13 Hands-on

15:35 Coffee break

17:15 Coffee and Cake

Wednesday 28 May

14 Metrics and Evaluation II

15 Decision Trees

10:45 Coffee break

12:30 Lunch break

16 Random Forest

17 Random Forest Hands-on

15:30 Coffee break

Thursday 29 May

18 Regression

10:45 Coffee break

12:30 Lunch break

19 Regularization

15:30 Coffee break

20 Regression methods Hands-on

17:30 Dinner

Friday 30 May

21 Dimensionality Reduction

22 Feature Engineering

10:45 Coffee break

12:30 Lunch break

23 Dimensionality Hands-on

24 ML project I: Data handling

15:30 Coffee break

Saturday 31 May

25 ML project II: Supervised learning

10:45 Coffee break

26 ML project III: Supervised learning

12:30 Lunch break

27 Nonlinear Regression

15:30 Coffee break

28 Nonlinear Regression Hands-on

Sunday 1 June

11:00 Excursion

Monday 2 June

29 k-Means, DBSCAN

10:45 Coffee break

30 Clustering Hands-on

12:30 Lunch break

31 Clustering Hands-on

32 Ensembles

15:30 Coffee break

Tuesday 3 June

33 Pattern search

34 Pattern search Hands-on

10:45 Coffee break

12:30 Lunch break

35 ML project IV: Unsupervised learning

15:30 Coffee break

Wednesday 4 June

36 Kernel methods

10:45 Coffee break

12:30 Lunch break

37 Introduction to Neural Networks

38 Neural Networks Optimization Algorithms

15:30 Coffee break

Thursday 5 June

39 CNN, RNN, LSTM

10:45 Coffee break

40 Fine tuning Neural Networks

12:30 Lunch break

41 Deep learning Hands-on

42 ML project V: Deep learning

15:30 Coffee break

Friday 6 June

43 ML project VI: Deep learning

44 Exam: full ML project

10:45 Coffee break

12:30 Lunch break

45 Technical Imnterview

15:00 Closing ceremony