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Machine Learning Overview

Machine Learning is a sub-field of artificial intelligence that uses data to train predictive models.

Training and Inference

Machine learning problems follow a two-phase flow:

  • Training: The model learns from data — adjusting its parameters to minimize error (supervised learning) or maximize reward (RL). This phase is computationally expensive and done offline.
  • Inference: The trained model is deployed and makes predictions on new, unseen inputs. Parameters are frozen — the model applies what it learned. This phase must often be fast and cheap, since it runs in production (e.g., serving predictions in real time).

Types of machine learning

  1. Supervised learning - learns from labeled training data.
  2. Unsupervised learning - learns from unlabeled training data.
    • principal component analysis, clustering.
  3. Reinforcement learning - An agent interacts with an environment and learns to take action by maximizing a cumulative reward.
    • Q-Learning, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO)

Machine learning problems

  1. Regression - predicting a continuous value attribute.
    • Example: predicting house prices
  2. Classification - predicting a discrete value.
    • Classification is further categorized as binary or multi-class classification.
    • Binary Example: predicting pass or fail, benign or malignant, spam or not spam, hot dog or not hot dog :-)
    • Multi-Class Example: Handwritten Digit Recognition (0 through 9) mnist, Image classification with 1,000 classes ImageNet-1k
  3. Ranking - predicting the relative order or preference of a set of items contextually.
    • Example: search engine results, or movie recommendations

Models that predict labels from inputs (as in the problems above) are called discriminative; models that learn the data distribution to synthesize new samples (diffusion, LLMs) are generative.

Additional Machine learning techniques

  • Transfer learning is storing knowledge gained while solving one problem and applying it to a different but related problem.

    • fine tuning is additional training to a base model for a specific task.
    • LoRA (Low-Rank Adaptation) is a fine-tuning method that freezes the base model's weights and trains small low-rank matrices that are added to existing layers.

  • Semi-Supervised learning trains on a mix of mostly unlabeled with a small labeled subset data.

  • Self-supervised learning is a form of unsupervised learning where training labels are constructed automatically from the data itself.

    • Autoregressive LLM pretraining (next word prediction), and masked image modeling.

  • Metric learning trains a model to produce embeddings where distance reflects similarity. A siamese network passes two inputs through identical networks with shared weights and compares the resulting embeddings. Trained with contrastive loss (pull matching pairs together, push non-matching pairs apart) or triplet loss (anchor, positive, negative).

    • Example: face verification, signature verification

Classes