MSc thesis supervision
I am supervising students who conduct research in information retrieval and learning analytics; combinations of the two areas (“search as learning”) are especially interesting to me. For in information retrieval, ongoing benchmark competitions give you a good idea of hot-topic tasks and research directions:
If you are interested in learning analytics, have a look at the proceedings of the first and second edition of the ACM Learning At Scale conference - they contain many interesting contributions in the area of massive open online learning.
Below are the resources I have developed for my courses (some are more up-to-date than others): Big Data Processing, Web and Database Technology and Information Retrieval.
Big Data Processing
Since 2013/2014 I have been teaching the second year Bachelor course Big Data Processing at TU Delft (with 2016/17 being the last time for now). The course covers a range of technologies in the Hadoop ecosystem after a short excursion into the streaming world; I created the material based on a number of great books, including Mining of Massive Datasets, Data-Intensive Text Processing with MapReduce, Hadoop: The Definite Guide, Programming Pig and ZooKeeper.
Slides - 2016/17 Edition
- Streams I
- Streams II
- Algorithm design for MapReduce
- Pig I
- Pig II
- Graph algorithms
- 2 more lecture on Spark completed this course.
Assignments - 2016/17 edition
- Assignment 1: Streaming
- Assignment 2: Streaming and Hadoop
- Assignment 3: Hadoop
- Assignment 4: Pig data
- Assignment 5: Pig data
- Assignment 6: Giraph
- Assignment 7: Spark
A Sample of Previous Exams
- 24 questions on streaming
- 32 questions on MapReduce/Hadoop
- 10 questions on graphs and Giraph
- 12 questions on Pig/Pig Latin
Web (and Database) Technology
Since 2013/2014 I have also been teaching the first year Bachelor course Web and Database Technology at TU Delft, together with Alessandro Bozzon. I teach the Web technology part, which turned out to be quite a challenge due to the wide variety of skill sets our incoming students possess (some work as Web developers, others have never written a single line of HTML before the start of this course). To level the playing field for the more than 300 students we have every year, we rely on the Learning Web App Development book, which introduces the modern Web stack in an accessible and practical manner. The lectures build on the material introduced in the book.
Web Slides - 2014/15 Edition
- HTML and Web app design
- A mix of technologies
- Cookies and sessions
- Securing your application
A Sample of Previous Exams
- Midterm 2014/15 (includes database questions, covered in the other course half)
- Final 2014/15 (includes database questions, covered in the other course half)
Slides - 2017/18 Edition
After a few years of not teaching IR, I am back at it. This time, the Information Retrieval course covers core IR topics for half of the lectures and NLP topics (taught by Nava Tintarev) for the other half.
- IR evaluation
- Retrieval models
- Query refinement
- Interactive IR
- Personalization in (Web) search
- Neural models
- Learning to rank
I have also written a blog post about the IR project setup.
Slides - 2011/12 Edition
In 2011/12 I taught my core area of research in a Master course Information Retrieval at TU Delft. This course was my first excursion into the use of Hadoop & Co as part of my teaching, thanks to a grant from Amazon and their (at the time) “AWS Education” scheme - $3500 to allow students to use a real Hadoop cluster for their experiments.
The course material is quite old by now, but it may still be useful to some. It was also my first venture into the teaching of large classes, the structure and design of the course certainly reflects that.