Machine Learning
Overview

Machine Learning

Machine learning is the discipline of developing algorithms that enable computational systems to learn from data and make informed predictions.

This course provides a rigorous survey of machine learning techniques, from fundamental supervised and unsupervised paradigms to advanced probabilistic and sequential models.

Topics Covered

  1. Supervised and Unsupervised Learning: The two primary paradigms of learning.
  2. Issues in Machine Learning: Fundamental challenges like overfitting and the curse of dimensionality.
  3. Linear Models for Regression: Predicting continuous values using maximum likelihood and Bayesian approaches.
  4. Linear Models for Classification: Separating classes using projections and generative models.
  5. Probabilistic Discriminative Models: Directly modeling posterior probabilities with logistic regression.
  6. Kernel Methods and SVMs: Mapping data to higher dimensions to solve non-linear problems.
  7. Clustering and Mixture Models: Finding patterns in unlabeled data using EM and GMMs.
  8. Sequential Data: Modeling time-dependent data with Markov Chains and HMMs.
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