Video Courses
AI: Introduction and Practical Examples
An introduction to Artificial Intelligence (AI), use of AI in society, supporting learning, and AI ethical/legal issues
In this course, you will learn what is behind the buzzword “artificial intelligence”. You will be aware of how artificial intelligence has influenced the society so far, and what the future possibilities are. Besides, you will get hands-on experience with a range of AI tools that might help you in everyday life, studying or research. Starting with ChatGPT, you will progress to AI tools supporting literature review and research. Finally, you will face some fundamental questions concerning ethical and legal issues related to AI usage.
What will you learn in the course?
A background of Artificial Intelligence
The impact of AI on everyday life
How to use selected AI tools
Priciples of ethical usage of AI
What are the requirements or prerequisites for taking the course?
No experience needed. You will learn everything you need to know.
Who is this course for?
Anyone interested in the functioning of the contemporary world.
Machine Learning
in Practice
An introduction to machine learning and a practical example of its application, using handwritten digit recognition
The course consists of two main sections.
In the first section, we will explain what machine learning (ML) is, what methods are used in ML, what datasets are used in train and evaluation processes, how to prepare data for the model, and what parameters and hyperparameters are.
The second section is a case study. In this part you will learn, how to create, train and implement a ML model. We will start with a short introduction to the programming environment. Next, we will explain and prepare the data for training. Then, we will build the ML model, train it using a train dataset and evaluate using a test dataset. Finally, we will create our own data and check if the model is able to recognise it. In this case study we will use the Python programming language, and tensorflow and keras libraries to create the model. For the dataset we will use MNIST – a set of handwritten digits with labels, so that our example will fall into the category of supervised learning tasks.
What will you learn in the course?
What machine learning is and how it works
Some important concepts concerning machine learning
How to prepare data for a machine learning model
How to prepare data, create the machine learning model, train it, evaluate it and use for your own data
What are the requirements or prerequisites for taking the course?
No experience needed. You will learn everything you need to know.
Who is this course for?
Anyone curious about machine learning who doesn’t know how to start.
Introducing Exploratory Data Analysis and Supervised Machine Learning with R
An introduction to exploratory data analysis and supervised machine learning with R using the Titanic dataset
The course consists of three main sections.
The first section provides a concise introduction to R and R Studio. Instructions on downloading and installing R and R Studio are provided. R is widely used in the data science community. R is the programming language, and R Studio is the integrated development environment. The R Notebook is a feature of R Studio that allows the execution of chunks of code. The code for this course is available as an R Notebook that will enable students to run and modify code.
The second section introduces the Titanic data set. It shows how exploratory data analysis may be done in R. Visualizations will be used to understand the data set and its implications.
The third section discusses machine learning. The problem discussed is how to predict the survival of Titanic passengers. Three models will be tested: Conditional Inference Tree, Naïve Bayes, and k-Nearest Neighbor. The models will be used as black boxes, their inner workings are not essential here, but the results will be shown and commented upon.
What will you learn in the course?
Basic use of R and R Studio.
How to do fundamental exploratory data analysis.
Practical machine learning using three different models.
What are the requirements or prerequisites for taking the course?
Some basic programming experience is required.
Who is this course for?
Anyone interested in data analysis and predictions using machine learning.
Acknowledgement
The project “Alignment of ICT Related Curricula with Labour Market Expectations” benefits from a grant of EUR 117 602 received from Iceland, Liechtenstein and Norway under EEA Grants. The aim of the project is to align the ICT educational offer with the labour market expectations.
Projekt „Dostosowanie programów nauczania ICT do oczekiwań rynku pracy” korzysta z dofinansowania o wartości 117 602 EUR otrzymanego od Islandii, Liechtensteinu i Norwegii w ramach Funduszy EOG. Celem projektu jest dostosowanie oferty edukacyjnej ICT do oczekiwań rynku pracy.