Practical Machine Learning

Machine learning (ML) is a rapidly growing field within artificial intelligence (AI) that focuses on building systems capable of learning from data. Instead of being explicitly programmed with detailed rules, the ML models identify patterns and make predictions or decisions based on historical data. This approach has revolutionized many industries, including healthcare, finance, marketing, and technology, enabling applications like personalized recommendations, fraud detection, and speech recognition. As the volume of data continues to grow, understanding ML concepts and techniques becomes increasingly important for anyone interested in working with data science or building intelligent systems.

This three hour online webinar is meant to give a comprehensive introduction to the fundamental principles and practical aspects of ML. It will start from the fundamentals of ML including basic concepts, ML types, and representative applications of ML, and then progress to practical techniques for data preprocessing, model selection, training, evaluation, and assessment. Participants will explore supervised (classification and regression) and unsupervised (clustering and dimensionality reduction) tasks using varied ML algorithms, such as k-nearest neighbors (KNN), linear/logistic regressions, decision tree, random forest, support vector machine (SVM), naive bayes, k-means, and neuron networks.

Feel free to join (and leave) whenever depending on your interest. The online event is open for all present and prospective Berzelius users.

Place: Online via Zoom. The link will be provided upon registration.
Time: 9:00 - 12:00, Thu Oct. 9th, 2025

If you don’t receive the NAISS training newsletter, check with General contact below for Zoom link.

Tentative Schedule

Prerequisites

To ensure a smooth learning experience, participants should have:

• basic proficiency in Python programming (variables, loops, functions) and some libraries like NumPy, Pandas, and Matplotlib/Seaborn
• familiarity with basic statistics and linear algebra concepts (e.g., mean, median, standard deviation, vectors, matrices)
• (optional) an active project on Berzelius if you plan to follow along on the system

Registration

Register HERE

General contact

Phone: +46 (0) 13-28 22 51 (Yonglei Wang)
E-mail: yonglei.wang@liu.se