IPPR 005

Pattern recognition

• recognizing patterns of interest oin data
• applications
  • image and video analysis
  • speech analysis
  • natural language processing
  • genomic research and bioinformatics
  • data mining and analytics for business, finance, marketing, trade
  • network traffic analysis
  • analysis of Web data
  • social media analysis

ML Problems

• Classification: predict a categorical value from an array of numerical/categorical features.
  • The input is assigned to the class with the highest score/probability.
• Regression: predict a numerical value from an array of numerical/categorical features.
• Clustering: group numerical data homogeneously into clusters.

Probabilistic Classifier

• The class scores are simply bounded between 0 and 1 and add up to 1 over all classes.
• probability of class $c$ given input $x$, or $p(c|x)$.
  • $p(c = \text{apple}|x) = 0.7$
  • $p(c = \text{banana}|x) = 0.2$
  • $p(c = \text{orange}|x) = 0.1$
• All contemporary deep learning classifiers are probabilistic.
• Training objective: assign the largest possible probability to the correct labels.

Hyperparameters

• Type of model: logistic regression, CNN, random forest, SVM etc.
• Size of a mask (3x3, 5x5) or of feature vector (100, 200)
• Number of clusters
• All discrete choices and any coefficient within the loss function.

Datasets

• Training set: it is used with the loss function to automatically find the optimal parameters for various, arbitrarily chosen values of the hyperparameters
• Validation set: it is used to find the best values for the hyperparameters (best performance evaluation metric)
• Test set: it is used with the chosen parameters and hyperparameters to measure and report the final model’s accuracy/performance

Popular algorithms

• K-means
• K-medoids, or PAM (partitioning around medoids)
• Other female-named clustering algorithms:
  • AGNES
  • DIANA
  • DAISY

Models

• U-Net: Classify individual pixels rather than entire images
• SMILETrack: track objects in video having a certain shape

• OpenAI CLIP: Trained with pairs of images and captions, and at run time is able to classify images into any category of choice

• Stable Diffusion