Fundamentals of AI and ML
What you will learn
In this domain, you learn the relationship between artificial intelligence (AI), machine learning (ML), and deep learning (DL), the differences between supervised, unsupervised, and reinforcement learning, and the general lifecycle of running an ML project. It is a foundational domain that accounts for 20% of the AIF-C01 exam and forms the basis for everything that follows.
Key points
- The containment relationship AI ⊃ ML ⊃ DL - AI is the broadest concept, ML is one approach within AI, and DL is an ML approach using neural networks
- Supervised learning - learns from pairs of inputs and correct answers (labels). It is divided into classification (categorical) and regression (numerical)
- Unsupervised learning - discovers the structure of data without correct labels. Representative examples are clustering, dimensionality reduction, and anomaly detection
- Reinforcement learning - an agent interacts with an environment and learns actions that maximize reward
- The ML lifecycle - the repeating cycle of data collection, preprocessing, training, evaluation, deployment, and monitoring
- Overfitting - the phenomenon in which performance fits the training data but drops on test data. Care is needed to preserve generalization performance
- Inference - the process of giving a trained model new input to obtain predictions. There are real-time inference and batch inference
Terms and concepts
The relationship between AI, machine learning, and deep learning
AI is a broad concept that refers to technologies that mimic intelligent human behavior in general. ML is an AI approach that "automatically learns rules from data," and DL is an ML approach that "uses multilayer neural networks." Most generative AI is an application of DL.
Supervised learning and unsupervised learning
Supervised learning learns from labeled data and is used for image classification and sales forecasting. Unsupervised learning finds structure in unlabeled data and is used for customer segmentation and anomaly detection. Semi-supervised learning is an intermediate between the two.
Reinforcement learning
A technique in which an agent (the learner) selects actions toward an environment and learns a strategy that maximizes the reward obtained as a result. It is used in game AI, robot control, recommendation optimization, and RLHF (reinforcement learning from human feedback) for generative AI.
Overfitting and generalization
Overfitting is the phenomenon in which a model fits the training data too closely and performs poorly on unseen data. It is prevented by increasing the amount of data, regularization, dropout, and cross-validation. Conversely, underfitting is a state in which the model is too simple to learn even the training data.
Methods of inference
Real-time inference returns a prediction immediately for each API request (e.g., a chatbot). Batch inference processes a body of data all at once (e.g., predicting purchases for all customers overnight). Real-time offers low latency, while batch offers cost efficiency for large volumes of data.
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