Machine Learning

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
  • Support vector machines
    • Maximum margin classification
    • Soft-margin SVM
    • Constrained optimization
  • Kernel methods
    • Kernel trick
    • Kernelized linear regression
  • Deep learning
    • Feedforward neural networks
    • Backpropagation
    • Advanced architectures
    • Adaptive step-size selection
  • Dimensionality reduction
    • Principal component analysis
    • Singular value decomposition
    • Probabilistic PCA
  • Mixture models
    • Gaussian mixture models
    • K-means
    • Topic 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).

Schedule

  • Lecture:
    • Monday 10:00 - 11:45, room MW 2001
  • Lecture / Practical session:
    • Tuesday 12:15 - 13:45, room MW 0001
  • Homework discussion:
    • Wednesday 16:00 - 18:00, room MI 00.02.001
  • Q&A session:
    • Wednesday 12:00 - 14:00, room MI 02.11.018

Organizational details

  • Language: English
  • Intended audience:
    • Master students of the Informatics department (Including Data Engineering & Analytics program).
    • Not available for Information Systems (Wirtschaftsinformatik) students.
    • Only TUM students are allowed to write the exam.
  • New regulation: Please note that you can only include one of IN2064 / IN2332 in your curriculum
  • 8 ECTS
  • Grade bonus of 0.3 will be awarded to students who show sufficient work for at least 75% of the homework sheets. Note, that the grades 1.0, 4.3, 4.7 and 5.0 can't be improved.
  • All announcements and course materials will be published on Piazza. Please also use Piazza to ask questions, we won't answer questions sent by email.