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Technical University of Munich

Machine Learning

This award-winning introductory Machine Learning lecture teaches the foundations of and concepts behind a wide range of common machine learning models. It uses a combination of engaging lectures, challenging mathematical exercises, practically-oriented programming tasks, and insightful tutorials. The lecture was awarded with the TeachInf 2020 award.

The Machine Learning lecture for WS24/25 is planned to be held in-person.

All announcements will be made on the Piazza forum, which can be accessed via the link on the course's moodle page.
Please do not send any questions about organizational matters via e-mail.
If you have problems accessing the Moodle course, contact simon.geisler [at] in.tum.de .

Tentative list of topics

  • Introduction
    • What is machine learning?
    • Typical tasks in ML
  • k-Nearest neighbors
    • kNN for classification and regression
    • Distance functions
    • Curse of dimensionality
  • Decision trees
    • Constructing & pruning decision trees
    • Basics of information theory
  • Probabilistic inference
    • Parameter estimation
    • Maximum likelihood principle
    • Maximum a posteriori
    • Full Bayesian approach
  • Linear regression
    • Linear basis function models
    • Overfitting
    • Bias-variance tradeoff
    • Model selection
    • Regularization
  • Linear classification
    • Perceptron algorithm
    • Generative / discriminative models for classification
    • Linear discriminant analysis
    • Logistic regression
  • Optimization
    • Gradient-based methods
    • Convex optimization
    • Stochastic gradient descent
  • Deep learning
    • Feedforward neural networks
    • Backpropagation
    • Structured data: CNNs, RNNs
    • Training strategies
    • Frameworks
    • Advanced architectures
  • Support vector machines
    • Maximum margin classification
    • Soft-margin SVM
  • Kernel methods
    • Kernel trick
    • Kernelized linear regression
  • Dimensionality reduction
    • Principal component analysis
    • Singular value decomposition
    • Probabilistic PCA
    • Matrix factorization
    • Autoencoders
  • Clustering
    • k-means
    • Gaussian mixture models
    • EM algorithm

Literature

  • Pattern Recognition and Machine Learning. Christopher Bishop. Springer-Verlag New York. 2006.
  • Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press. 2012

Prerequisites

  • Good understanding of Linear Algebra, Analysis, Probability and Statistics.
  • Programming experience (preferably in Python).
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