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
Tuesday 12:15 - 13:45, room MW 0001
Tutorial (in-class exercises and 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.
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.
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.