Yekun's Note

Machine learning notes and writeup.

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Machine Learning Basics

A short introduction about machine learning.

Type of machine learning (ML)

Predictive/ supervised learning

  • Goal: learn a mapping from inputs $x$ to outputs $y$, given a labeled set of input-output pairs , a.k.a. training set.
  • Conditional density estimation, i.e. build models for


    a.k.a. pattern recognition.
  • Binary classification
  • Multi-class classification
  • Multi-label classification (viewed as doing multiple binary predictions)

The mode of the distribution , a.k.a. MAP (maximum a posteriori) estimate:

  • Given a probabilistic output, compute the “best guess” as to the “true label”:


Descriptive/ unsupervised learning

  • Goal: Only given inputs , find “interesting patterns” in the data (a.k.a. knowledge discovery).
  • Unconditional density estimation, i.e. build models for

Popular deep unsupervised generative models:

  • GANs
  • VAEs
  • Fully visible belief networks (FVBN)

Discovering clusters

Dimension reduction: Clustering data into groups. Let $K$ denote the number of clusters, we estimate the distribution over the number of clusters, $P(K|\mathscr{D})$, which tells us if there are subpopulations within the data.

Discovering latent factors

Reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data.

Discovering graph structure

Measure a set of correlated variables, discover which ones are most correlated with which others. We learn the graph structure from the data, i.e. compute .

Matrix completion

Missing data (NaN, “not a number”) completion.

Image inpainting

“Fill in” holes in an image with realistic texture. This can be tackled by building a joint probability model of the pixels, given a set of clean images, and then inferring the unknown variables (pixels) given the known variables (pixels).

Collaborative filtering

Key idea: the prediction is not based on features of the movie or user (although it could be), but merely on a ratings matrix $\mathbf{X}(m,u)$ with user $u$ of movie $m$.

upload successful

Market basket analysis

Reinforcement learning

  • Goal: learn how to act / behave when given occasional reward or punishment signals (e.g. how a baby learns to walk).

Basic ML concepts

Parametric v.s. non-parametric models

Parametric models

  • Models have a fixed number of parameters.
  • Cons: strong assumptions about the nature of the data distributions.

Non-parametric models

  • The number of model parameters grow with the amount of training set.
  • Example: $K$-nearest neighbor classifier
  • The curse of dimensionality