Prof. Roberto Grossi Dr. Filippo Geracii

This week the teaching activity is suspended, as requested by our Rector. Hence the classes of March 5 and 6 are canceled. Next class is Tue, March 10, regular time, but lectures will be given in streaming. More details follow.

You student, what can you do next for getting a lecture?

1. Join the class on Google Classroom (use Android/iOS or connect to the Algorithm Design link), and use the code below:
   
   
2. Click on the link for streaming on Google Meet for attending the classes. Please note that we keep our schedule for time, the only difference is that you have connect to the link instead of physically coming to the room.

• Class hours: Tue 14:00‑16:00 (Fib N1), Thu 11:00‑13:00 (Fib L1), Fri 11:00‑13:00 (Fib N1)
• Office hours: Fri 13:00-16:00 or by appointment.

The advanced nature of this course focuses on developing algorithmic design skills, exposing the students to complex problems that cannot be directly handled by standard libraries (being aware that several basic algorithms and data structures are already covered by the libraries of modern programming languages), thus requiring a significant effort in problem solving. These problems involve all basic data types, such as integers, strings, (geometric) points, trees and graphs as a starting point. The syllabus is structured to highlight the applicative situations in which the corresponding algorithms can be successfully employed, making references to software applications and libraries. The level of detail in each argument can change year-by-year, and will be decided according to requests coming from other courses in the curriculum and/or specific issues arising in, possibly novel, applicative scenarios.

Written exam:

1. choose one of the topics discussed in class
2. write a very short to-do list and ask the instructor for approval
3. if the instructor suggests some mods, modify the to-do list according to the instructor's comments and repeat step 2
4. if the chosen topic and the to-do list are approved, expand the to-do list into a more detailed to-do list and repeat step 3
5. make a written report in English and submit it to the instructor (recall to add at least 20% new content, when compared to what seen in class)
6. meet the instructor to read together the report and get some comments on it
7. make the necessary mods
8. use Jupyter Lab to prepare your report (so as to mix Markdown, LaTeX and Python code). More info inside Google Classroom.

Suggested reading: some useful tips for scientific writing in English (first two sections) by J.S. Vitter.

Example of interaction: student and instructor discussing the report's content and structure.

Oral exam: topics discussed in class, please read the references in the notes.

Caveat: Several topics are the outcomes of recent advancements in the field, and thus the course material mostly consists in research papers or book chapters.

Randomization is a powerful tool to solve large-scale problems. After introducing the concept of randomized algorithms and hashing, we consider some applications, such as data streaming algorithms, a field emerged in the last decade. Here data flow as a stream and one-pass algorithms with limited memory can process it. We focus on the count-min sketch paradigm and its applications. [Note: to refresh the basic notions on counting and probability, please refer to Appendix C in Cormen-Leiserson-Rivest-Stein's book “Introduction to Algorithms”, 3rd ed., MIT Press. Concentration bounds are explained in these class notes.]

• The screen snapshots shown during the classes are available in the Google Classroom shared drive.