252-0220-00L Introduction to Machine Learning
| Semester | Spring Semester 2021 |
| Lecturers | A. Krause, F. Yang |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch |
Courses
| Number | Title | Hours | Lecturers | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 252-0220-00 V | Introduction to Machine Learning Findet im ETA F 5 mit Videoübertragung ins ETF E 1 statt | 4 hrs |
| A. Krause, F. Yang | ||||||||||||
| 252-0220-00 U | Introduction to Machine Learning Q&A session Wed 16-17 | 2 hrs |
| A. Krause, F. Yang | ||||||||||||
| 252-0220-00 A | Introduction to Machine Learning No presence required. | 1 hrs | A. Krause, F. Yang |
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 - 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, F. Yang |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| 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. Simple non-programmable calculator |
| 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 | 800 at the most |
| Priority | Registration for the course unit is until 07.03.2021 only possible for the primary target group |
| Primary target group | Integrated Building Systems MSc (062000)
Robotics, Systems and Control MSc (159000) Mechanical Engineering MSc (162000) Electrical Engin. + Information Technology BSc (228000) Quantum Engineering MSc (235000) Electrical Engin. + Information Technology MSc (237000) Biomedical Engineering MSc (238000) Computer Science BSc (252000) Computational Biology and Bioinformatics MSc (262200) Computer Science MSc (263000) Doctorate Computer Science (264002) DAS ETH in Data Science (266000) Computer Science (Mobility) (274000) Mathematics BSc (404000) Computational Science and Engineering BSc (406000) Quantitative Finance MSc (435000) Statistics MSc (436000) Computational Science and Engineering MSc (438000) |
| Waiting list | until 14.03.2021 |
