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.

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).

Schedule

• Due to the current situation, we cannot offer in-person lectures. We will upload videos of lectures and tutorials, and provide pointers to other reference materials. Additionally, we will offer slots for online, live Q&A sessions (timeslots will be provided later).
• Please also note the new lecture period for this semester (02.11.2020 – 12.02.2021)
• All material will be provided via Moodle.
• Discussions will take place via Piazza. Please also use Piazza to ask questions, we won't answer questions sent by email.
• Registration for the course will open soon (most likely in early October).

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.
• 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 exercises. Note, that the grades 1.0, 4.3, 4.7 and 5.0 can't be improved.