252-0220-00L Introduction to Machine Learning
| Semester | Spring Semester 2019 |
| Lecturers | A. Krause |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Previously called Learning and Intelligent Systems. |
Courses
| Number | Title | Hours | Lecturers | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-0220-00 V | Introduction to Machine Learning Die Vorlesung findet jeweils (Di 13-15 und Mi 13-15) im HG E 7 mit Videoübertragung im HG E 5 und HG E 3 statt. | 4 hrs |
| A. Krause | ||||||||||||||||||
| 252-0220-00 U | Introduction to Machine Learning | 2 hrs |
| A. Krause | ||||||||||||||||||
| 252-0220-00 A | Introduction to Machine Learning No presence required. | 1 hrs | A. Krause |
Catalogue data
| Abstract | The course introduces the foundations of learning and making predictions based on data. |
| Learning objective | The 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-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - 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 approaches to unsupervised learning (Gaussian mixtures, EM) |
| Literature | Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press |
| Prerequisites / Notice | Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - 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 credits | 8 credits |
| Examiners | A. Krause |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| Mode of examination | written 120 minutes |
| Additional information on mode of examination | 70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function. Die Prüfung kann am Computer stattfinden / The exam might take place at a computer. The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Introduction to Machine Learning (252-0220-00L). Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show. |
| Written aids | Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | Information |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| Places | 900 at the most |
| Waiting list | until 10.03.2019 |
