Algorithms and Data Structures - Spring 2023
Fabio Di Lauro
, Bojan Lazarevski,
, Claudio Milanesi
Tuesday 10:30–12:30, Thursday
for details and updates
Instructors' Office Hours:
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Algorithms and data structures are fundamental to computer science.
They are the essence of computer programs. Also, the performance of
any software system depends on the efficiency of its algorithms and
data structures. Designing and analyzing algorithms is therefore
crucial for the development of software systems. More generally, the
study of algorithms provides insight into the nature of problems and
their possible solutions, independent of programming language,
programming paradigm, computer hardware, or any other implementation
aspect. The objective of this course is to provide students with the
knowledge and skills necessary to design and reason about algorithms,
and to understand some of the most fundamental algorithms and data
structures, their strengths and weaknesses, and their suitability in
The course will cover basic notions of complexity, including
asymptotic analysis of worst-case and average complexity, big-O,
little-o, omega, and theta notation, polynomial reductions, poly-time
verification vs. solution, NP and P complexity classes; general
algorithm strategies such as brute force, greedy, divide-and-conquer,
and dynamic programming; common algorithms, including elementary
numeric computations, searching and sorting, elementary graph
algorithms, and string matching; basic data structures, including
stacks, queues, linked lists, and rooted trees; more advanced data
structures, including B-trees, heaps, hash tables, and structures
representing disjoint sets and dictionaries.
See this page
for assessment criteria and
general course policies.
Learning Material and Other Useful Links
Lectures and Related Material
An introduction to elementary, algorithmic programming in Python.
This topic will be covered over a series of lectures.
Course introduction: course organization and policies; Example of
the Fibonacci sequence: a bad algorithm and a good algorithm;
implementation-independent behaviors and algorithmic complexity.
Complexity under the RAM model. Asymptotic growth
of functions: big-O, Omega, and Theta notations.
Basic elements for the analysis of algorithms:
complexity and correctness of insertion-sort; worst-, best-, and
average-case complexity; loop invariants.
Divide and conquer strategy: binary search, merge,
merge sort, recursive multiplication, median and k-smallest
Quick-sort. Heaps and heap-sort.
Elementary data structures and hash tables.
Binary search trees. Randomized binary search trees.
Review or binary trees. Height of a BST in the
average case: simulation and visualization.
Red-Black trees: introduction.
B-Trees. Data structures in secondary storage:
modeling disk access. Structure, search, and insertion in
B-trees. Relation with red-black trees.
Graphs: representation and elementary algorithms.
Minimal spanning trees; Disjoint sets.
Greedy algorithms: general strategy, activity
selection, Huffman codes.
Dynamic programming: examples, and general strategy.
Basic elements of complexity theory: polynomial-time
algorithms and problems; the P complexity class; the NP class;
polynomial-time reduction and NP-completeness; satisfiability.