index.Rmd

---
title: "Algorithms and Data Structures"
subtitle: "Course information"
author: "Georgios Gousios"
date: "`r format(Sys.time(), '%d %B %Y')`"
output: revealjs::revealjs_presentation
bibliography: bibliography.bib
csl: ieee.csl
nocite: |
  @Cormen09,@Louridas17
---

```{r child="header.Rmd", include=FALSE}
```

## General description

Algorithm: named after محمد بن موسى الخوارزمی ([Muhammad ibn Musa al-Khwarizmi](https://en.wikipedia.org/wiki/Muhammad_ibn_Musa_al-Khwarizmi))

: Any well-defined computational procedure that
takes some value, or set of values, as input and produces some value, or set of values,
as output. [@Cormen09]

Data Structure:

: A particular way of storing and organising data in memory so that it can be
used efficiently.

Algorithms and data structures are fundamental notions in computer science.
This course will teach you how to use data structures to represent data and
algorithms to process them in efficient ways. The course uses the Python
programming language.

```{python}
def hello():
  print "Hello World"

hello()
```

The course examines the implementation of basic data structures, such as arrays,
lists, stacks, sets, trees and graphs along with algorithms for efficiently
creating, storing, searching and traversing them. It also touches upon
more advanced topics such as genetic algorithms and dynamic programming.

## Learning Objectives

This course enables the student to:

* Understand, explain, and implement standard data structures.
* Understand, explain, and implement standard algorithms.
* Apply standard data structures and algorithms to solve programming tasks.
* Analyse and compare implementations with respect to their time and space complexity.
* Understand and explain advanced topics in algorithm design

## Course Organization

* **5 ECTS**: This means that you need to devote at least 140 hours of study
  for this course.

* **Lectures**: The course consists of 14 2-hour lectures. You are not required,
  but you are **strongly encouraged**, to attend.

* **Lecturers**: The course is supervised by Georgios Gousios, who is responsible
for the content, assignments and exams. Several lectures will help with the lectures.

* **Homework**: In the homework assignments, you will have to write code or
reply to open questions. You will always work in groups of 2.

* **Groups**: The groups will be generated randomly and will be different
per assignment.

* **Labs**: 4 hours per week, designed to help you work together and get support
  from teaching assistants.

* **Teaching Assistants**: Teaching assistants will be available during
  lab hours to help you with solving your assignments. Do be active in asking
  questions, but don't expect them to provide you with solutions to the
  exercises.

## Assignments

You can find the course assignments linked through this page.
*All assignments are mandatory.*

Your submission material is a
[Jupyter notebook](http://jupyter.org) including the full assignment text,
your solutions and the results of running your solutions on the provided
datasets.

You submit your assignments **THE DAY BEFORE** the deadline. For example, the
deadline for the first assignment is on 28/11. You must submit your assignment
by Nov 27, 23:59.

To submit your assignment, you must export the Jupyter notebook to PDF (Save as...)
and upload it to the designated BrightSpace folder (you can find those on
BrightSpace). Name your submission file as `student_id1-student_id2.pdf`,
replacing `student_id1` and `student_id2` with your TU Delft IDs. It is enough
if one member of each group uploads a version of the assignment.

The assignments are signed-off and graded by TAs.
You are expected to be at the lab at the designated timeslot
assigned to your group. Timeslots will be announced well in advance.
During your timeslot, you must be able to demonstrate a notebook with your
solution running live. The TAs will compare your results with the ground truth
and grade your solution in place.

**Late submission**: All submissions must be handed in time, with no
exceptions. Any late submission will be discarded and will be graded with 0.
In case of *provable* sickness, please contact the course
teacher to arrange a case-specific deadline.

## Contents

|Week |Lecture | Topic                | Lecturer | Assignment (Deadline)|
|:---:|:------:|----------------------|----------|----------------------|
| 1 | 1 | [Course introduction](course-intro.html), [Recursion](recursion.html) | GG | |
| 1 | 1 | [Algorithm Analysis](complexity.html)   | GG | |
| 2 | 1 | [Arrays, Queues and Stacks](basic-structures.html) | MK | Doubly-linked lists ([jypyter](assignment-lists.ipynb), [html](assignment-lists.html), [solutions](assignment-lists-solutions.html)) (28/11/2017) |
| 2 | 2 | [Lists, Sets](basic-structures.html) | MK | |
| 3 | 1 | [Trees: basic concepts and binary Trees](trees.html) | JH | Red-Black Trees ([jupyter](assignment-rbtrees.ipynb), [html](assignment-rbtrees.html), [solutions](assignment-rbtrees-solutions.html)) (12/12/2017) |
| 3 | 2 | [More Trees: AVL and B+ Trees](trees.html) | JH | |
| 4 | 1 | [Graphs](graphs_1.html) (Representation and Traversal) | PK | Graph algorithms ([jupyter](assignment-graphs.ipynb), [html](assignment-graphs.html), [solutions](assignment-graphs-solutions.html)) (9/01/2018)|
| 4 | 2 | [Graph algorithms](graphs_2.html) (Shortest paths, Topological sorting) | PK ||
| 5 | 1 | [Sorting](searching-sorting-algos.html) | JH | Searching ([jupyter](assignment-searching.ipynb), [html](assignment-searching.html), [solutions](assignment-searching-solutions.html)) (15/01/2018)|
| 5 | 2 | [Searching](searching-sorting-algos.html) | JH ||
| 6 | 1 | [Strings and string search](strings.html)  | GG ||
| 6 | 2 | [Genetic algorithms](optimizations.html)  | MS ||
| 7 | 1 | No Lecture | -- ||
| 7 | 2 | [Overall Q/A](recap.html) | GG ||

Lecture notes alternative formats (may be obsolete/contain errors):

* [Lecture notes in GitBook format](http://gousios.org/courses/algo-ds/book/preface.html)

**Lecturers**

* GG: Georgios Gousios
* PV: Pavel Kucherbaev
* JH: Joseph Hejderup
* MK: Maria Kechagia
* MS: Mozhan Soltani

## Assessment

In order to pass the course, you must obtain a passing grade (6+) to all the
assessment criteria specified below:

* Assignments (40%): Grade calculated as mean grade for all assignments.
All individual assignments must have a passing grade.
* Written Exam (60%)

#### Example exam material

* [Model exam](model-exam.html), [Solutions](model-exam-solutions.html)

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```