"Man is a 'homo discens,' a learning being. People learn as long as they live. Life is inseparably connected with learning." Horst Siebert

What did You learn today ?

What was the most important thing You learned this year ?

Implicit learning is the process of acquiring knowledge or skills unconsciously, without intentional effort or explicit awareness of what is being learned. It typically occurs through repeated exposure to patterns, stimuli, or behaviors, allowing individuals to internalize rules or structures without being able to articulate them directly.

Observation in the context of learning refers to the process of acquiring knowledge, skills, or behaviors by perceiving the actions of others or the dynamics within an environment. This learning occurs without direct engagement but through experiencing of external stimuli, events, or behaviors.

Unsupervised learning is a type of machine learning where a model is trained on data without labeled outcomes. The system analyzes and identifies patterns or structures in the data, such as clustering or associations, without explicit guidance on what to look for. It is often used for tasks like anomaly detection, clustering, and dimensionality reduction.

No direct guidance :: Learner L must identify patterns or relationships from raw input on her own.

Pattern discovery :: Observation often involves recognizing and understanding patterns in the environment, which mirrors what happens in unsupervised learning.

Learning from the environment :: L derives insights from the world or data without external labels or supervision.

Imitation in humans, especially in children, is the process of learning by observing and replicating the actions, behaviors, or expressions of others. It is a fundamental mechanism for acquiring social, cognitive, and motor skills, allowing children to mimic gestures, language, or problem-solving techniques. Through imitation, children learn cultural norms, communication patterns, and even complex tasks without explicit instruction. It is crucial for early development, as it helps children integrate into their social environment and build understanding by modeling behaviors they see in parents, peers, and others.

Learning is building A model of THE world.

Experiential learning is a process of learning through direct experience, where individuals engage in activities, reflect on their actions, and apply what they’ve learned to new situations. Rather than solely reading or listening, learners actively participate, often experimenting, making mistakes, and adapting.

In learning and problem-solving, a "trial" is a single attempt or effort to reach a goal or find a solution. Each trial involves testing an idea or making a change, then observing the result. If the trial doesn’t succeed, adjustments are made based on what was learned, and a new trial begins. This process, called "trial and error," continues until the desired outcome is achieved. Trials are key in learning because they allow us to explore, adapt, and improve with each attempt, reducing mistakes and moving closer to success over time.

In learning and problem-solving, an "error" is the difference between what we aimed to achieve and the actual result. It shows how far we are from the desired outcome and helps us understand what needs adjusting. Errors aren’t failures; they’re valuable feedback, guiding us to make improvements in each new attempt. By identifying and reducing errors through practice and adjustments, we gradually get closer to the goal. In this way, errors are essential to learning, as they highlight what doesn’t work and point us toward what might.

In learning, skill-building, and habit formation, repetition is the process of repeatedly practicing or performing a task. This continuous repetition reinforces memory, builds familiarity, and over time, transforms skills into habits or reflexes, making actions more automatic and effortless. Through repetition, connections in the brain are strengthened, allowing tasks to be completed with less conscious effort. 

Game provides a closed system whereby we are allowed to commit errors (& learn from them) without major consequences for real life.

What is Your most favorite game / way of playing ?

Any game You would like to bring & play with Your colleagues during the Congress ?

Supervised learning is a type of machine learning where a model is trained on labeled data to learn the mapping between input features and corresponding outputs. The goal is to enable the model to make accurate predictions or classifications on unseen data by minimizing the error between its predictions and the true labels. Common tasks include regression (predicting continuous values) and classification (assigning categories). Supervised learning relies on a training dataset with known inputs and outputs and evaluates performance using a separate test dataset. Examples include spam email detection, image recognition, and speech-to-text systems.

In supervised machine learning, *testing* (or *inference*) is the process of evaluating a trained model's ability to make accurate predictions on new, unseen data. During this phase, the model is given data points with *features* (inputs like size or color) but without the labels it was trained on. The model uses the patterns it learned during training to predict the labels for this data. The results are then compared to the actual labels (if available) to measure the model's performance using metrics like accuracy or precision. Inference is the final application of the model to make real-world predictions.

Supervised learning parallels human learning through its reliance on guidance from labeled examples, similar to how humans learn with feedback. For instance, when a child learns to identify objects, they receive input (the object) and a corresponding label (e.g., "dog" or "apple") from a teacher or parent. Mistakes are corrected, reinforcing the connection between input and label, much like how supervised learning algorithms adjust their predictions based on errors.

A classifier in machine learning is a model or algorithm designed to categorize data into predefined groups or labels. It takes input data, analyzes its features, and assigns it to a specific class based on learned patterns from training data. For example, a classifier might identify whether an email is spam or not spam, or recognize handwritten digits. Classifiers are essential in supervised learning tasks and operate by minimizing errors in predictions through training on labeled datasets. Common types include neural networks, support vector machines, decision trees etc.

In supervised machine learning, training is the process of teaching a model, like a classifier, to make accurate predictions by learning patterns from labeled data. Each data point in the training set includes features (characteristics or inputs that describe the data, like size or color) and a corresponding label (the correct output or category). The model uses this data to adjust its internal parameters, minimizing the error between its predictions and the actual labels. This is done through algorithms like gradient descent. The goal is to generalize from the training data, enabling the classifier to make accurate predictions on new, unseen data.

Binary classifiers are evaluated by comparing their predictions to the actual outcomes using a confusion matrix. This is a table with four categories: True Positives (TP), where the classifier correctly predicts a positive outcome; True Negatives (TN), where it correctly predicts a negative outcome; False Positives (FP), where it wrongly predicts a positive; and False Negatives (FN), where it misses a positive case. Metrics like accuracy (overall correctness), precision (focus on positives), and recall (how well positives are found) are calculated from this matrix, helping to assess the classifier’s performance.

In supervised machine learning, *validating* is the process of fine-tuning and assessing a model's performance during training to ensure it generalizes well to unseen data. Unlike testing, validation occurs on a separate *validation set*, distinct from both training and testing data. The model uses the *features* of this set to make predictions, which are compared to the actual labels to calculate metrics like accuracy or loss. This helps monitor overfitting or underfitting and guides adjustments to model parameters or hyperparameters (e.g., learning rate or regularization). Validation ensures the classifier is optimized before its final evaluation on the test set.

Machine Learning

Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make decisions by interacting with an environment. Instead of being told what to do, the agent takes actions and receives feedback in the form of rewards or penalties. The goal is to maximize cumulative rewards over time by discovering an optimal strategy, known as a policy. RL is inspired by trial-and-error learning in humans and animals, where behavior improves through experience. It’s particularly useful for tasks with sequential decision-making, such as robotics, game playing, and autonomous systems, where actions impact not only immediate rewards but also future outcomes.

Experiential learning, Unsupervised learning, Supervised learning, Classifiers & Machine Learning ...

Supervised learning resembles a structured classroom environment, where explicit feedback is given for each example (e.g., a teacher correcting a student's answers). In contrast, reinforcement learning mirrors experiential learning, where feedback comes as rewards or penalties after actions, guiding behavior toward long-term goals. For instance, a child learning to ride a bike might fall (penalty) or stay balanced (reward), gradually improving through trial and error.

Conditioning is a learning process where an individual forms associations between stimuli or behaviors and their outcomes. It can be divided into two main types:

Classical Conditioning: Involves pairing a neutral stimulus with a meaningful one to elicit a similar response (e.g., Pavlov’s dogs salivating at the sound of a bell).

Operant Conditioning: Involves learning through rewards or punishments, where behaviors are strengthened or weakened based on their consequences (e.g., Thorndike’s Law of Effect).

In machines, reinforcement learning (RL) is implemented using an agent-environment framework. The agent interacts with an environment by taking actions based on a policy (a strategy for decision-making). The environment provides feedback in the form of rewards or penalties, guiding the agent to improve its actions. Key components include a reward function to evaluate outcomes, a value function to estimate long-term benefits of actions, and exploration strategies to balance learning new behaviors versus exploiting known rewards.

Q-learning is a model-free reinforcement learning algorithm that enables an agent to learn an optimal policy for decision-making. It works by estimating the Q-values (action-value function), which represent the expected cumulative reward for taking an action in a given state and following the best future actions. The agent updates Q-values iteratively using the formula:

DRL is a type of machine learning where an agent learns to make decisions by trial and error, guided by rewards or penalties, using deep neural networks. Unlike traditional methods, which struggle with complex environments, DRL allows machines to learn directly from raw data, like images or game screens. The neural network helps the agent recognize patterns and improve its decisions over time. DRL has achieved impressive results in tasks like playing video games (e.g., Atari, AlphaGo), controlling robots, and developing self-driving cars, making it a powerful tool for solving real-world problems involving sequential decision-making

"Cells that fire together, wire together."

Social learning is "a process in which individuals learn by observing the behaviors of others, imitating them, and experiencing the consequences of these actions." (Bandura, 1977)

Albert Bandura’s Social Learning Theory (1977) revolutionized psychology by emphasizing that learning occurs in a social context through observation, imitation, and modeling. He argued that individuals do not solely learn through direct reinforcement (as behaviorism suggests) but also by observing others, processing information cognitively, and making choices based on expected outcomes.

Peer learning

Active engagement:::Attention:::Error Feedback:::Consolidation

Active engagement

Attention

Error Feedback

Consolidation

Human-Machine Peer Learning

Teaching, Pedagogy, Didactics

Artificial Teacher Avatars

Educational Systems

Extended Educational Environments

The Congress