## Carlos Cotrini Jimenez: Catalogue data in Autumn Semester 2022 |

Name | Dr. Carlos Cotrini Jimenez |

Address | Lehre D-INFK ETH Zürich, CAB H 32.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |

ccarlos@inf.ethz.ch | |

URL | https://inf.ethz.ch/personal/ccarlos |

Department | Computer Science |

Relationship | Lecturer |

Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||
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252-0535-00L | Advanced Machine Learning | 10 credits | 3V + 2U + 4A | J. M. Buhmann, C. Cotrini Jimenez | |||||||||||||||||

Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | ||||||||||||||||||||

Objective | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | ||||||||||||||||||||

Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | ||||||||||||||||||||

Lecture notes | No lecture notes, but slides will be made available on the course webpage. | ||||||||||||||||||||

Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | ||||||||||||||||||||

Prerequisites / Notice | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points. | ||||||||||||||||||||

252-0845-00L | Computer Science I | 5 credits | 2V + 2U | C. Cotrini Jimenez, M. Fischer | |||||||||||||||||

Abstract | The course covers the basic concepts of computer programming. | ||||||||||||||||||||

Objective | Basic understanding of programming concepts. Students will be able to write and read simple programs and to modify existing programs. In the course "Computer Science I", the competency of programming is taught, applied and examined. Furthermore modeling is taught and applied. | ||||||||||||||||||||

Content | variables, types, control structures, functions, scoping, recursion, object-oriented programming. The programming language is Python. | ||||||||||||||||||||

Lecture notes | The slides and lecture notes will be made available for download on the course website. | ||||||||||||||||||||

Literature | Learn to Code by Solving Problems A Python Programming Primer Daniel Zingaro Python Crash Course A Hands-On, Project-Based Introduction to Programming Eric Matthes | ||||||||||||||||||||

Competencies |
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252-0847-00L | Computer Science | 5 credits | 2V + 2U | C. Cotrini Jimenez, F. Friedrich Wicker | |||||||||||||||||

Abstract | The course covers the fundamental concepts of computer programming with a focus on systematic algorithmic problem solving. Taught language is C++. No programming experience is required. | ||||||||||||||||||||

Objective | Primary educational objective is to learn programming with C++. After having successfully attended the course, students have a good command of the mechanisms to construct a program. They know the fundamental control and data structures and understand how an algorithmic problem is mapped to a computer program. They have an idea of what happens "behind the scenes" when a program is translated and executed. Secondary goals are an algorithmic computational thinking, understanding the possibilities and limits of programming and to impart the way of thinking like a computer scientist. | ||||||||||||||||||||

Content | The course covers fundamental data types, expressions and statements, (limits of) computer arithmetic, control statements, functions, arrays, structural types and pointers. The part on object orientation deals with classes, inheritance and polymorphism; simple dynamic data types are introduced as examples. In general, the concepts provided in the course are motivated and illustrated with algorithms and applications. | ||||||||||||||||||||

Lecture notes | English lecture notes will be provided during the semester. The lecture notes and the lecture slides will be made available for download on the course web page. Exercises are solved and submitted online. | ||||||||||||||||||||

Literature | Bjarne Stroustrup: Einführung in die Programmierung mit C++, Pearson Studium, 2010 Stephen Prata, C++ Primer Plus, Sixth Edition, Addison Wesley, 2012 Andrew Koenig and Barbara E. Moo: Accelerated C++, Addison-Wesley, 2000 |