TU Delft > Algorithmics > Neil Yorke-Smith > Teaching
My broad teaching portfolio includes functional courses such as operations and scheduling, and cross-functional courses such as algorithms and statistics. My current main teaching interest is applied artificial intelligence.
MSc project? I offer a set of ideas for TU Delft students on data-driven optimisation, AI applied to real decision problems, or agent-based simulation. (See also the topics of current and previous students.) For a MSc thesis internship with a company, I am always interested to supervise projects with companies like Adyen, ASML, CGI, KLM, NXP, ORTEC, Picnic, TNO and Vanderlande, as well as tech start-ups. TU Delft BSc students looking for a research or honours opportunity are also welcome to contact me.
I teach in the Electrical Engineering, Mathematics and Computer Science faculty at the Delft University of Technology (TU Delft). Students are welcome to come to my office in person.
A student taking one of these courses? For help, post a question on Answers, contact a TA or attend the extra lab sessions. For administrative matters, contact the course email address (see Brightspace).
In 2024-25 Q1 the BSc class CSE1300: Reasoning and Logic:
CSE1300: Reasoning and Logic is about proving the logical validity of arguments. What is a valid argument? When is an argument logically valid and when is it not? How can we determine whether an argument is logically valid? How can we derive a logically valid conclusion from the premises? Or how can we prove that a conclusion is not a logical consequence of the premises? In this course we will see a number of basic proof techniques. The application of these techniques will be practiced by proving and rejecting simple mathematical theorems. We will also look at elementary set theory. Together, the course provides tools that have important applications across mathematics, informatics, philosophy, and beyond. The course is supported by an open textbook.
In 2024-25 Q1 the new MSc class DSAIT4010: Probabilistic AI and Reasoning:
DSAIT4010: Probabilistic AI and Reasoning is motivated by artificial intelligence and data science dealing with a variety of complex problems ranging from face recognition to inventory management and controlling humanoid robots. For such tasks it is not immediately clear how to manually program good solutions. Instead, many approaches to successfully dealing with such complex problems are based on modelling the problem in a formal framework (such as a logic, graphical model or Bayesian network), and applying corresponding reasoning or optimization techniques to find solutions. This course aims to introduce a number of such frameworks and how they can be used to model real-world problems and often also the uncertainties that arise in them. This includes discussing relations between these models and their associated methods like search, inference, learning and optimization. The specific topics include logical modelling, constraint satisfaction problems, graphical models, Bayesian networks, as well as temporal reasoning and learning via Markov decision processes and reinforcement learning.
In 2024-25 Q2 the new MSc elective CS4530: Modelling and Problem Solving:
CS4530: Modelling and Problem Solving covers major (combinatorial) solving techniques and their core algorithms, namely constraint programming, integer programming, SAT and first-order logic, and classical planning. The course focuses on modelling practical problems, expressing them in the survey paradigms; algorithms for solving such problems are covered in the follow-up CS4535: Constraint Solving course. Furthermore, the course explores how data can be used to improve and accompany such models. Lastly, the course focuses on conducting proper empirical evaluations.
In 2024-25 Q3 the BSc elective CSE3300: Algorithms for NP-Hard Problems:
CSE3300: Algorithms for NP-Hard Problems studies combinatorial optimization problems such as scheduling or optimally playing a board game, which are often NP-hard. Most of the algorithms in earlier courses run in polynomial time and cannot be directly used for solving NP-hard problems. In this course you will learn some new algorithms and use these, but also concepts from earlier courses on algorithm design, to solve NP-hard problems.
In 2024-25 Q4 the new MSc elective DSAIT4220: Research in Intelligent Decision Making:
DSAIT4220: Research in Intelligent Decision Making believes that intelligent decision making is a key skill of computational agents. Research on this topic focuses on building models and algorithms that enable AI systems to take appropriate decisions. Building upon theoretical knowledge gained in the Probabilistic Decision Making or Optimisation and Reasoning themes, students collaborate in small groups on a distinct research project per group, for instance on decision-making problems in transport, logistics or smart energy grids. Purely algorithmic challenges will also be provided.
In 2024 and in 2022 I organised a course on machine learning and combinatorial optimisation for the Institute for Information and Knowledge Systems (SIKS) in Utrecht, Netherlands.
In 2022 I gave a tutorial on certification of reliable autonomous systems at the 19th Advanced Course on AI (ACAI) and 2nd TAILOR Network Summer School in Barcelona, Spain.
In 2021 I gave a tutorial on agent-based social simulation at the 1st TAILOR Network Summer School (online).
In 2019 I taught a course on constraint programming for the Institute for Programming research and Algorithmics (IPA) in Eindhoven, Netherlands.
In 2018 I taught a course on agent-based social simulation at the 20th European Agent Systems Summer School in Maastricht, Netherlands.
Advice on doing research? See Jan van Gemert's guidelines.
From 2018-2024 I taught the MSc elective CS4210-A: Algorithms for Intelligent Decision Making, and its follow-on MSc seminar CS4210-B: Intelligent Decision Making Project:
CS4210-A: Algorithms for Intelligent Decision Making recognises that decision making is at the centre of artificial intelligence. This course gives you practical skills on a solid theoretical base. The course looks at solving mathematical models of NP-hard discrete optimisation problems. These kinds of problems lie at the heart of AI techniques such as planning, machine learning and mechanism design, and more generally combinatorial optimisation. You will learn about a range of modelling techniques from boolean satisfiability to constraint programming, and how advanced solvers for these models work. The course has plenty of real-world case studies as well as theoretical results. The course leads to CS4210-B: Intelligent Decision Making Project in Q4.
From 2018-2020 I taught the MSc class IN4301: Advanced Algorithms:
IN4301: Advanced Algorithms asks how to solve instances of intractable problems in reasonable time. By the end of this course, students will have knowledge of and experience with some advanced algorithmic techniques: exact algorithms for NP-hard problems, approximation techniques, and linear programming relaxation techniques. The course is an excellent foundation for studying AI-based optimization.
In 2017-18 I taught the MSc seminar CS4175: Automated Scheduling:
CS4175: Automated Scheduling concerns the effective planning and management of time and resources. It has a crucial role in business, industry, government, and education. Scheduling lies at the intersection of Operations Research, Artificial Intelligence, and Computer Science. In this course we will study real-life scheduling problems from an algorithmic perspective, paying attention also to the challenges of data and system development. We go beyond classical scheduling problems studied in traditional Operations Research, such as the job shop scheduling problem, and focus on realistic problems with complex constraints and objectives.
From 2009-2016, I taught in the Business Information and Decision Systems track of the Suliman S. Olayan School of Business at the American University of Beirut.
DCSN200: Operations Management provides a foundation for understanding the operations of a firm. One of the main objectives of this course is to recognize the immense competitive advantages companies can draw from an efficient management of operations. The focus will be mainly on the systematic planning, design, and operations of some of the main processes required for the production of goods and the delivery of services. Operations management touches upon many vital business functions of an organization including product and service design, customer order management, processes design and improvement, capacity and material planning, quality control, inventory and supply chain management.
DCSN205: Managerial Decision Making is a spreadsheet-based introduction to the tools and techniques of modern managerial decision making. It addresses formulation of models that can be used to analyze complex problems taken from various functional areas of management, including finance, marketing, operations, and human resources. The goal is to understand how business decisions are reached, what tradeoffs are made, and how outcomes depend on the underlying data. A broad range of analytical methods is covered, including linear programming, integer linear programming, decision analysis, decision trees, queues and Monte Carlo simulation. Software packages like Excel, Risk Solver, Tree Plan and Arena will be used. DCSN200 is recommended as a prerequisite.
DCSN217: Total Quality Management with Excel focuses on the concepts related to quality in all aspects of enterprise operations with special emphasis on the customer. Total Quality Management (TQM) is a comprehensive and fundamental rule or belief for leading and operating an organization, aimed at continually improving performance over the long term by focusing on customers while addressing the needs of all stakeholders. It is both a philosophy and a set of guiding principles that represent the foundation of a continuously improving organization. The bottom line of TQM is results: increased productivity, efficiency, customer satisfaction, and world-class performance. This course will present the various TQM frameworks, concepts, and quality improvement tools necessary for implementing the quality culture that characterizes world-class organizations of the 21st century.
In 2012-13, I supervised IIB Statistical Modelling in the mathematical tripos of the University of Cambridge.
IIB Statistical Modelling takes an applied perspective to statistics. Lectures cover linear and generalized linear models, which provide a powerful and flexible framework for the study of the relationship between a response and one or more explanatory variables. In practical classes, we learn how to implement the techniques covered in the lectures, by covering several real data sets. Extensive use is made of the programming language R. This course should appeal to a broad range of students, including those considering further research in any aspect of statistics and those considering careers in data-intensive careers (investment banking, insurance, etc).
From 2006-2009, I taught the class CS227: Reasoning Methods in Artificial Intelligence in the Department of Computer Science at Stanford University.
CS227: Reasoning Methods in Artificial Intelligence is a second class in AI, covering methods for (largely) non-probabilistic reasoning in propositional satisfiability, constraint satisfaction, temporal reasoning, and planning. It is complementary to CS228: Probabilistic Models in Artificial Intelligence. Assessment is by group programming projects. The course is appropriate for graduate students (both Masters and PhD students), and for advanced undergraduates with a special interest in AI. Students are assumed to have taken basic courses in AI, algorithms, data structures, and programming, or to have equivalent background in these areas.