252-0220-00L  Introduction to Machine Learning

SemesterSpring Semester 2018
LecturersA. Krause
Periodicityyearly recurring course
Language of instructionEnglish
CommentPreviously called Learning and Intelligent Systems

Prof. Krause approves that students take distance exams, also if the exam will take place at a later time due to a different time zone of the alternative exam place.
To get Prof. Krause's signature on the distance exam form please send it to Rita Klute, rita.klute@inf.ethz.ch.

Catalogue data

AbstractThe course introduces the foudations of learning and making predictions based on data.
ObjectiveThe course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent)
- Linear classification: Logistic regression (feature selection, sparsity, multi-class)
- Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression; k-NN
- The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference)
- Statistical decision theory (decision making based on statistical models and utility functions)
- Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions)
- Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE)
- Bayesian networks and exact inference (conditional independence; variable elimination; TANs)
- Approximate inference (sum/max product; Gibbs sampling)
- Latent variable models (Gaussian Misture Models, EM Algorithm)
- Temporal models (Bayesian filtering, Hidden Markov Models)
- Sequential decision making (MDPs, value and policy iteration)
- Reinforcement learning (model-based RL, Q-learning)
LiteratureTextbook: Kevin Murphy: A Probabilistic Perspective, MIT Press
Prerequisites / NoticeDesigned to provide basis for following courses:
- Advanced Machine Learning
- Data Mining: Learning from Large Data Sets
- Probabilistic Artificial Intelligence
- Probabilistic Graphical Models
- Seminar "Advanced Topics in Machine Learning"

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits8 credits
ExaminersA. Krause
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThe final grade will be determined by the written final exam. The project accounts for 30% of the grade, but it will only be counted if it exceeds your exam grade
Written aidsTwo A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


252-0220-00 VIntroduction to Machine Learning
Die Vorlesung findet jeweils (Di 13-15 und Mi 13-15) im ML D 28 mit Videoübertragung im ML E 12 statt.
4 hrs
Tue13-15ML D 28 »
13-15ML E 12 »
Wed13-15ML D 28 »
13-15ML E 12 »
29.05.13-15HG E 3 »
13-15HG F 30 »
A. Krause
252-0220-00 UIntroduction to Machine Learning2 hrs
Mon15-17HG D 1.2 »
Tue15-17HG D 1.2 »
Wed15-17CAB G 11 »
Fri13-15ML D 28 »
A. Krause
252-0220-00 AIntroduction to Machine Learning
No presence required.
1 hrsA. Krause


No information on groups available.


There are no additional restrictions for the registration.

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