CSEN 1022

4 lecture hours


4  ECTS credits

Machine Learning

Abstract

  • This course covers a wide range of machine learning algorithms including supervised, unsupervised and reinforcement learning paradigms. The course does not assume any prior knowledge of machine learning. However, background in probability theory and linear algebra is recommended.

Outline

    • Probability Theory and Linear Algebra Review
    • Supervised Learning
      • Linear Classifiers
        • Discriminant Functions
        • Probabilistic Generative Models
        • Probabilistic Discriminative Models
      • Non-linear Classifiers
        • K-nearest Neighbor Classifier
        • Decision Trees
    • Unsupervised Learning
      • K-means Clustering
      • Fuzzy C-means Clustering
      • Hierarchical Clustering
      • Gaussian Mixture Models
      • Spectral Clustering
    • Feature Extraction and Dimensionality Reduction
      • Principal Component Analysis
      • Independent Component Analysis
    • Reinforcement Learning
      • Q Learning
      • Non-deterministic Rewards
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