252-0220-00L  Introduction to Machine Learning

Courses

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)
Prerequisites / Notice	Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning"
Competencies	Subject-specific Competencies Concepts and Theories assessed Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making assessed Media and Digital Technologies assessed Problem-solving assessed Project Management assessed Social Competencies Communication fostered Cooperation and Teamwork fostered Personal Competencies Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	8 credits
Examiners	F. Yang, F. Perez Cruz
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. 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. Please note: this exam will likely take place on a Saturday during summer examination sessions
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

Groups

Restrictions

Offered in

Programme	Section	Type
Biomedical Engineering Master	Recommended Elective Courses	W
Biomedical Engineering Master	Recommended Elective Courses	W
Computational Biology and Bioinformatics Master	Data Science	W
DAS in Data Science	Foundations Courses	W
Electrical Engineering and Information Technology Bachelor	Engineering Electives	W
Electrical Engineering and Information Technology Master	Foundation Core Courses	W
Electrical Engineering and Information Technology Master	Core Subjects	W
Geomatics Master	Complementary Electives	W
Computer Science Bachelor	Major: Information and Data Processing	O
Integrated Building Systems Master	Specialised Courses	W
MAS in Medical Physics	Core Courses	W
Mechanical Engineering Bachelor	Mechatronics and Robotics	W
Mechanical Engineering Master	Mechanics, Materials, Structures	W
Mechanical Engineering Master	Robotics, Systems and Control	W
Mathematics Bachelor	Selection: Further Realms and Some UZH Courses	W
Physics Master	General Electives	W
Quantitative Finance Master	MF (Mathematical Methods in Finance)	W
Quantum Engineering Master	Electives	W
Computational Science and Engineering Bachelor	Core Courses	W
Computational Science and Engineering Bachelor	Robotics	W
Computational Science and Engineering Master	Robotics	W
Robotics, Systems and Control Master	Core Courses	W
Science, Technology, and Policy Master	Data and Computer Science	W
Statistics Master	Subject Specific Electives	W