Basics of Machine Learning

• Artificial Intelligence (AI) is a broad field encompassing the development of intelligent agents that can reason, learn, and act autonomously.
• AI aims to create machines capable of performing tasks that typically require human intelligence.

Machine Learning

• Machine Learning (ML) is a subset of AI focused on enabling systems to learn from data without explicit programming.
• ML algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform the task.
• Supervised learning involves training a model on labeled data, where the input and desired output are provided.
• Regression predicts a continuous output, while classification predicts a categorical output.
• Common supervised learning algorithms include linear regression, logistic regression, support vector machines (SVM), and decision trees.
• Unsupervised learning involves training a model on unlabeled data to discover patterns or structures.
• Clustering groups similar data points together, while dimensionality reduction reduces the number of variables while preserving important information.
• Common unsupervised learning algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA).
• Reinforcement learning involves training an agent to make decisions in an environment to maximize a reward.
• The agent learns through trial and error, receiving feedback in the form of rewards or penalties.
• Key components of reinforcement learning include the agent, environment, states, actions, and rewards.
• Common reinforcement learning algorithms include Q-learning and SARSA.

Neural Networks

• Neural Networks are a class of ML models inspired by the structure and function of the human brain.
• They consist of interconnected nodes (neurons) organized in layers.
• An artificial neural network (ANN) is composed of layers of interconnected nodes, called artificial neurons, which process and transmit signals.
• The connections between neurons have weights that are adjusted during the learning process.
• Deep learning involves neural networks with multiple layers (deep neural networks).
• Deep learning has achieved significant success in various tasks, such as image recognition, natural language processing, and speech recognition.
• Convolutional Neural Networks (CNNs) are specifically designed for processing grid-like data, such as images.
• CNNs use convolutional layers to extract features from the input data.
• Recurrent Neural Networks (RNNs) are designed for processing sequential data, such as text or time series.
• RNNs have feedback connections that allow them to maintain a memory of past inputs.
• Long Short-Term Memory (LSTM) networks are a type of RNN that can effectively learn long-range dependencies.

Natural Language Processing

• Natural Language Processing (NLP) is a field of AI focused on enabling computers to understand, interpret, and generate human language.
• NLP tasks include: text classification, sentiment analysis, machine translation, question answering, and text generation.
• Text preprocessing techniques include tokenization, stemming, lemmatization, and removing stop words.
• Word embeddings, such as Word2Vec and GloVe, represent words as vectors in a high-dimensional space.
• These embeddings capture semantic relationships between words.
• Recurrent Neural Networks (RNNs) and Transformers are commonly used for NLP tasks.
• Transformers, such as BERT and GPT, have achieved state-of-the-art results in many NLP benchmarks.

Computer Vision

• Computer Vision is a field of AI focused on enabling computers to "see" and interpret images and videos.
• Computer vision tasks include image classification, object detection, image segmentation, and image generation.
• Convolutional Neural Networks (CNNs) are the most commonly used architecture for computer vision tasks.
• Transfer learning, using pre-trained models on large datasets, is a common technique in computer vision.
• Object detection involves identifying and localizing objects within an image.
• Image segmentation involves partitioning an image into multiple regions or segments.

Ethics in AI

• Ethics in AI involves addressing the ethical implications and societal impact of AI technologies.
• Key ethical considerations include: bias, fairness, transparency, accountability, and privacy.
• AI bias can arise from biased training data or biased algorithms.
• This can lead to discriminatory outcomes.
• Fairness in AI aims to ensure that AI systems treat all individuals and groups equitably.
• Transparency in AI refers to the ability to understand how AI systems make decisions.
• Accountability in AI involves assigning responsibility for the actions and decisions of AI systems.
• Privacy in AI involves protecting individuals' personal data from unauthorized access and use.
• Regulations and guidelines are being developed to promote ethical AI development and deployment.
• Explainable AI (XAI) aims to make AI systems more transparent and understandable to humans.
• Robustness refers to the ability of AI systems to maintain their performance under various conditions and inputs, resisting adversarial attacks and noise.
• Security in AI focuses on protecting AI systems from malicious attacks and ensuring their safe operation.

Basic Overall AI for University Exams

• AI is a broad field aiming to create intelligent agents.
• Machine Learning is a subfield of AI that allows systems to learn from data.
• Neural Networks are machine learning models inspired by the human brain, used in deep learning.
• Natural Language Processing enables computers to understand and generate human language.
• Computer Vision enables computers to "see" and interpret images.
• Ethics in AI addresses the ethical implications and societal impact of AI.
• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data.
• Reinforcement learning trains agents to make decisions in an environment to maximize a reward.
• Convolutional Neural Networks (CNNs) are used for image processing.
• Recurrent Neural Networks (RNNs) are used for sequential data processing.
• Transformers have achieved state-of-the-art results in NLP.
• Key ethical considerations include bias, fairness, transparency, accountability, and privacy.
• XAI aims to make AI systems more transparent and understandable.