Informatics 2 - Introduction to Algorithms and Data Structures

INF2-IADS: Schedule and Course Materials for Semester 1 (Autumn 2023)

mcorey — Thu, 03 Aug 2023 13:03:24 +0000

INF2-IADS: Schedule and Course Materials for Semester 1 (Autumn 2023) mcorey Thu, 03/08/2023 - 14:03

Below you can find the schedule for Semester 1. You may also access the material from last year if you would like a preview of what's coming, but do keep in mind that this will eventually be replaced by new material - similar but updated.

Week (commencing)	Date	Event	Lecturer	Topic	Reading
1 (18 Sept)	Tue 19 Sept	Lecture 1	Aris and John	Lecture 0 - Course admin Lecture 1 - Overview of course content Recording from previous years	Algorithms Illuminated 1.1., 10.1. CLRS 1
1 (18 Sept)	Thu 21 Sept	Lecture 2	John	Lecture 2 - Inefficient vs. efficient algorithms Add-on: Searching and sorting "the obvious way" Recording from previous years	CLRS 2.1, CLRS 2.3, CLRS 31.6 (second half) Algorithms Illuminated 1.4, 1.5. KT 2.1, 2.4. (Merging two sorted lists), 5.1. (only page 210, but read through 5.1 if you are interested).
2 (25 Sept)	Mon 25 Wed 27 Sept	Lab 1		Lab Sheet 1: Getting started in Python Sheet 1 solutions Lab Sheet 2: Writing programs in Python Sheet 2 solutions
	Tue 26 Sept	Lecture 3	John	Lecture 3 - Asymptotic notation: Little o and omega Recording from previous years	Algorithms Illuminated 2 KT 2.2, 2.4 CLRS 3 (does everything) GGT 3.3, 3.4
	Thu 28 Sept	Lecture 4	John	Lecture 4 - More asymptotics: Big O, Omega, and Theta Recording from previous years	As for Lecture 3
3 (02 Oct)	Mon 02 Wed 04 Oct	Lab 2		Lab Sheet 3: Classes and more queue.txt (.py file not accepted by OpenCourse - sorry for inconvenience!) numbers1.txt, numbers2.txt, numbers3.txt, numbers4.txt, numbers5.txt Sheet 3 solutions
	Multiple dates	Tutorial 1		Tutorial 1 - Asymptotic Notation Solutions
	Tue 3 Oct	Lecture 5	John	Lecture 5 - Asymptotic analysis of sorting algorithms Recording from previous years	Roughgarden 1.5, 1.6 KT 2.4/4.1 CLRS 2.2
	Thu 5 Oct	Lecture 6	John	Lecture 6 - Representation of program data in memory Recording from previous years	CLRS 10.2, 10.3 Python documentation: Data model (just 3.1)
4 (09 Oct)	Multiple dates	Tutorial 1		Tutorial 2 - Analysis of Algorithms Solutions
	Tue 10 Oct	Lecture 7	John	Lecture 7 - Abstract data types: Lists, stacks, queues Recording from previous years	Array expansion: GTG 5.1-5.4, Sedgewick 1.4, CLRS 17.4 Stacks + queues: GTG 6.1, 6.2, 7.1, Sedgewick 1.3, CLRS 10
	Thu 12 Oct	Lecture 8	John	Lecture 8 - Sets, dictionaries and hashing Recording from previous years	Roughgarden 12.1-12.4 (recommended!), GTG 10.1, 10.2, Sedgewick 3.4
5 (16 Oct)	Tue 17 Oct	Lecture 9	John	Lecture 9 - Balanced trees Recording from previous years	Sedgewick 3.2 (first half) and 3.3 (second half) CLRS 12.1-3, 13.1-3
5 (16 Oct)	Thu 19 Oct	Lecture 10	John	Lecture 10 - Divide-conquer-combine and the Master Theorem Recording from previous years
6 (23 Oct)	Multiple Dates	Lab 3		Drop-in for coursework 1
	Multiple Dates	Tutorial 3		Data structures for lists and sets Solutions
	Tue 24 Oct	Lecture 11	Aris	Lecture 11 - Heapsort Lecture 11 - Heapsort (.key file, better animations) Recording from previous years (caution: different lecturer, different slides, presents all heap operations in Lecture 11 and Heapsort in Lecture 12).	CLRS 6.1-6.4 KT 2.5 (uses min-heap) Roughgarden 10.2, 10.3.1, 10.5 (caution: RG uses min-heap and the terms "Heapify" for Build-Max-Heap and implements Min-Heapify as part of "ExtractMin").
7 (30 Oct)	Multiple Dates	Lab 4		Drop-in for coursework 1
7 (30 Oct)	Tue 31 Oct	Lecture 12	Aris	Lecture 12 - Heap operations and Priority Queues Lecture 12 - Heap operations and Priority Queues (.key file, better animations) Recording from previous years (caution: different lecturer, different slides, presents all heap operations in Lecture 11 and Heapsort in Lecture 12).	CLRS 6.5 KT 2.5 (uses min-heap) Roughgarden 10.2, 10.5 (caution: RG uses min-heap and the terms "Heapify" for Build-Max-Heap and implements Min-Heapify as part of "ExtractMin").
8 (6 Nov)	Multiple Dates	Lab 5	*	Drop-in for coursework 1
	Multiple Dates	Tutorial 4	*	The Master Theorem and Heaps Solutions
	Tue 7 Nov	Lecture 13	Aris	Lecture 13 - Quicksort and the limitations of sorting algorithms Recording from previous years (caution: different lecturer, different slides, slightly different content).	CLRS 7.1-7.3, 8.1. CLRS 7.4 (only if you are interested) Roughgarden 5.1-5.4, 5.6 Roughgarden 5.5 (only if you are interested)
9 (13 Nov)	Tue 14 Nov	Lecture 14	Aris	Lecture 14 - Graphs, DFS, and BFS Lecture 14 - Graphs, DFS, and BFS (.pdf) Recording from previous years (caution: different lecturer, different slides, slightly different content).	KT 3.2., 3.3. Roughgarden 8.1., 8.2., 8.4. CLRS 20.1., 20.2., 20.3.
10 (20 Nov)	Multiple Dates	Tutorial 5	*	Sorting, BFS, and DFS Solutions
10 (20 Nov)	Tue 21 Nov	Lecture 15	Aris	Lecture 15 - Testing Bipartiteness and Topological Sort Lecture 15 - Testing Bipartiteness and Topological Sort (.pdf) Recording from previous years (caution: different lecturer, different slides, slightly different content).	KT 3.4., 3.6. Roughgarden 8.5. CLRS 20.4. Notes: Roughgarden and CLRS do not cover testing for bipartiteness. They also approach topological sort via DFS.
MATERIAL FROM PREVIOUS YEARS
10 (20 Nov)	varies	Tutorial 5	*	Quicksort and BFS
10 (20 Nov)	Tue 21 Nov	Lecture 15	Mary	Lecture 15 - Graphs II: DFS and Topological Sort Recording from previous years	CLRS 22.4

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INF2-IADS: Course Learning Outcomes and Outline

mcorey — Thu, 03 Aug 2023 12:47:52 +0000

INF2-IADS: Course Learning Outcomes and Outline mcorey Thu, 03/08/2023 - 13:47

Learning Outcomes

On successful completion of this course, you should be able to:

1. Explain both formally and informally the difference between "best", "expected", and "worst" case behaviour of an algorithm, and use asymptotic notation to analyse the time and space complexity of algorithms. Use recurrence relations to determine the time complexity of recursively defined algorithms.

2. Describe the properties, typical implementations, and example application use cases of abstract data types (e.g., stacks, queues, sets, dictionaries, priority queues) and discuss the costs and benefits of dynamic and static data structure implementations; use the above knowledge to justify the selection of appropriate data types in a range of settings.

3. Work with a range of data structures to implement basic algorithms given pseudocode or a task specification; perform empirical studies to compare the performance of different implementations of the same algorithm or data type on various input (or different algorithms for the same problem) and explain what can be learned from empirical analysis that cannot be learned from asymptotic analysis (and vice versa).

4. Describe various algorithmic strategies (e.g., brute-force, greedy, divide-and-conquer, recursive backtracking, dynamic programming) and give examples of each from a range of application areas including language processing and information retrieval. Hand-simulate a range of algorithms, including algorithms for searching, sorting, hashing, solving graph problems, and examples of dynamic programming. Give example applications that would use each algorithm and choose appropriate algorithms to use for example problems.

5. Define informally the classes P and NP and give examples of problems in NP. Explain the halting problem and its significance.

Course Outline

This course is an important foundation for all areas of Informatics.

It runs for the full year (10 credits in each semester), with approximately 15 lectures per semester. A mixture of tutorials and labs will be used to reinforce both mathematical and practical knowledge of algorithms and data structures, including differences between theoretical and empirical analysis.

Students' ability to implement and empirically analyse algorithms will be assessed via practical coursework, with an exam to assess other aspects of the course (knowledge and choice of existing algorithms and data structures, theoretical analysis, algorithmic strategies, and applications).

The following is an indicative list of topics covered:

Asymptotic notation and algorithmic analysis
Sequential data structures (lists, stacks, queues)
Basic and more advanced sorting algorithms
Tree data structures, heaps and priority queues
Hashing and dictionaries
Graphs and graph algorithms
Dynamic programming
The classes P and NP

Throughout, different specific algorithms and algorithmic strategies (such as divide-and-conquer, greedy, recursive backtracking, dynamic programming) will be introduced using real-world examples.

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INF2-IADS: Course Contacts

flittlet — Tue, 18 Jul 2023 14:49:52 +0000

INF2-IADS: Course Contacts flittlet Tue, 18/07/2023 - 15:49

Course Lecturers:

Aris Filos-Ratsikas (Course Organiser)
Office: IF-502
Drop-in hour: Wednesday 10.00-11.00

John Longley
Office: IF-415
Drop-in hour: Friday 10:30-11:30

Tutors:

Maksims Galkins
Andi Dicu
Aria Shrimpton
Arnav Bhargava
Dan Barber
Dugald MacIntyre
Luke Noonen
Michele Ciampi
Neel Amonkar
Souparna Mandal
Xinxian Tian
John Longley
Aris Filos-Ratsikas

Demonstrators:

Anant Gupta
Arthur Pan
Arun Gopalakrishnan
Litu Ou

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INF2-IADS: Assessment

flittlet — Tue, 18 Jul 2023 14:49:49 +0000

INF2-IADS: Assessment flittlet Tue, 18/07/2023 - 15:49

Assessment will be based on

6 short "quizzes", three in each semester. The first one is only for practice, the remaining ones contribute 2% each, for a total of 10%.
2 coursework assignments, one in each semester. Each coursework assignment contributes 15%.
A final exam, which contributes 60%.

More details about these can be found on the Assessment page on Learn.

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INF2-IADS: Resource List

flittlet — Tue, 18 Jul 2023 14:49:47 +0000

INF2-IADS: Resource List flittlet Tue, 18/07/2023 - 15:49

Informatics 2 - Introduction to Algorithms and Data Structures Resource List

You can access the reading list for this course by selecting the link above. In order to view some resources on the list, you must be logged in with your EASE account.

For more information on getting the most out of your courses Resource List, have a look at this video.

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INF2-IADS: Schedule and Course Materials for Semester 2 (Spring 2024)

flittlet — Tue, 18 Jul 2023 14:49:47 +0000

INF2-IADS: Schedule and Course Materials for Semester 2 (Spring 2024) flittlet Tue, 18/07/2023 - 15:49

Week (commencing)	Date	Event	Lecturer	Topic	Reading
1 (15 Jan)	Tue 16 Jan	Lecture 1	Aris	Lecture 16a - Semester 2 Introduction Lecture 16 - Greedy Algorithms: Interval Scheduling Lecture 16 - Greedy Algorithms: Interval Scheduling (.key file, better animations)	Kleinberg and Tardos 4.1 (online version from the library), 5.1. (printed version).
1 (15 Jan)	Fri 19 Jan	Lecture 2	Aris	Lecture 17 - Greedy Algorithms: Dijkstra's algorithm for shortest paths Lecture 17 - Greedy Algorithms: Dijkstra's algorithm for shortest paths (.key file, better animations) Recordings The algorithm Correctness Running Time	Kleinberg and Tardos 4.4 (online version from the library), 5.4. (printed version).
2 (22 Jan)	Tue 23 Jan	Lecture 3	Aris	Lecture 18 - Dynamic Programming: Weighted Interval Scheduling Lecture 18 - Dynamic Programming: Weighted Interval Scheduling (.pptx)	Kleinberg and Tardos 6.1
2 (22 Jan)	Fri 26 Jan	Lecture 4	Aris	Lecture 19 - Dynamic Programming: Subset Sum and Knapsack Lecture 19 - Dynamic Programming: Subset Sum and Knapsack (.key file, better animations)	Kleinberg and Tardos 6.4 Roughgarden 16.5
	Mon 22 Fri 26 Jan	Lab 6		Lab Sheet 4: Greedy Algorithms Solutions
3 (29 Jan)	Tue 30 Jan	Lecture 5	Aris	Lecture 20 - Dynamic Programming: The Bellman-Ford Algorithm for Shortest Paths Lecture 20 - Dynamic Programming: The Bellman-Ford Algorithm for Shortest Paths (.key file, better animations)	Kleinberg and Tardos 6.8.(see also 6.10 if you are interested in how to find negative cycles, when they exist). CLRS 22.1. Roughgarden 18.1, 18.2.
3 (29 Jan)	Fri 2 Feb	Lecture 6	John	Lecture 21 - Context-Free Languages and Grammars Pre-recorded video version (including song)	M. Sipser, Introduction to the Theory of Computation (3rd ed.), Section 2.1. Available online from UoE library. Wikipedia article on Context-free grammar Other resources as listed on Slide 21.
	Multiple dates	Tutorial 1		Tutorial 6 - Greedy and Dynamic Programming Solutions
4 (5 Feb)	Tue 6 Feb	Lecture 7	John	Lecture 22 - CYK Parsing for Context-Free Languages Pre-recorded video version
4 (5 Feb)	Fri 9 Feb	Lecture 8	John	Lecture 23 - Predictive Parsing Pre-recorded video version
	Multiple dates	Tutorial 2		Tutorial 7 - Dynamic Programming Solutions
	Mon 22 Fri 26 Jan	Lab 6		Lab Sheet 5: Dynamic Programming Solutions Bellman-Ford.txt WIS.txt
5 (12 Feb)	Tue 13 Feb	Lecture 9	Aris	Lecture 24 - Introduction to NP-completeness Lecture 24 - Introduction to NP-completeness (.key file, better animations)	Kleinberg and Tardos 8.1 (up to page 454), 8.4.
5 (12 Feb)	Fri 16 Feb	Lecture 10	Aris	Lecture 25 - NP-completeness of Vertex Cover Lecture 25 NP-completeness of Vertex Cover (.key file, better animations)	M. Sipser, Introduction to the Theory of Computation (3rd ed.), Section 7.5, proof of Thm 7.44. Available online from UoE library.
6 (26 Feb)	Tue 27 Feb	Lecture 11	Aris	Lecture 26 - NP-completeness: A closer look Lecture 26 - NP-completeness: A closer look (.key file, better animations)	Kleinberg and Tardos 8.3., 8.10. Roughgarden Chapters 19, 23 (read through).
	Multiple dates	Tutorial 3		Tutorial 8 - Parsing Algorithms and Polytime Reductions Solutions
7 (4 Mar)	Tue 5 Mar	Lecture 12	Aris	Lecture 27 - Greedy Approximation Algorithms Lecture 27 - Greedy Approximation Algorithms (.key file, better animations)	Kleinberg and Tardos 11.1 Roughgarden 20.1 Williamson and Shmoys, The Design of Approximation Algorithms, 1.1, 2.3. Available online from UoE library.
8 (11 Mar)	Tue 12 Mar	Lecture 13	Aris	Lecture 28 - (Fully) Polynomial-Time Approximation Schemes Lecture 28- (Fully) Polynomial-Time Approximation Schemes(.key file, better animations)	Kleinberg and Tardos 11.8 Williamson and Shmoys, The Design of Approximation Algorithms, 3.1. Available online from UoE library.
	Multiple dates	Tutorial 3		Tutorial 9 - Approximation Algorithms Solutions
	Mon 11 Fri 15 Mar	Lab 7		Lab Sheet 6: NP-completeness Solutions
9 (18 Mar)	Tue 19 Mar	Lecture 14	John	Lecture 29 - Intro to Computability Theory	M. Sipser, Introduction to the Theory of Computation (3rd ed.), Chapter 3. Available online from UoE library
10 (25 Mar)	Tue 26 Mar	Lecture 15	John	Lecture 30 - Unsolvable Problems Tutorial 10 - Register machines and computability Solutions

MATERIAL FROM LAST YEAR

Week	Date	Event	Lecturer	Topic	Reading
1	Tue 16 Jan (2024)	Lecture 16	Mary	Graphs III: Dijkstra's Algorithm video playlist, slides	CLRS Ed 3: 22.1, 24: (580-586), 24.3, 24.5 CLRS Ed 4: 20.1, 22.2, 22.3 and 24.5 AIgs.Illum: 9.1, 9.2, 9.3 and 10.4, 10.5
1	Thu 18 Jan	Lecture 17	Mary	Introduction to Dynamic Programming video playlist, slides	Algs.Illum: 16.4 (and maybe 16.1-16.3 for context) Wright's coin-changing paper (Uni library subscription)
2	varies	Lab 6	*	Lab sheet 4: Dynamic Programming in Python, coin_changing.py, solutions: coin_changing_sol.py, editdist_sol.py
	Tue 23 Jan	Lecture 18	Mary	Seam-Carving by Dynamic Programming (with seam-carving demo video) video playlist (parts 1-2), seam carving video, slides	CLRS 15.3, 15.4 (though they refer to problems we don't consider) GTG 13.3.2 (somewhat relevant) The original "Seam Carving" paper is here.
	Thu 25 Jan	Lecture 19	Mary	Edit distance by Dynamic Programming video playlist (parts 3-5), slides
3	varies	Lab 7	*	Lab sheet 5: Dijkstra's algorithm in Python
	varies	Tutorial 6	*	Dynamic Programming solutions, slides, Edit Distance trick (Raul Garcia) Jiaheng's slides part 1 (from 2022, please ignore Floyd), part 2 (knapsack)
	Tues 30 Jan	Lecture 20	Mary	Probabilistic Finite State Machines and the Viterbi Algorithm (last year's) video playlist, slides
	Thu 1 Feb	Lecture 21	John	Context-free Languages and Grammars last year's video playlist, slides
4	varies	Tutorial 7	*	Dynamic programming (cont'd) and Context-free Grammars solutions, slides, Jiaheng's 2022 slides
	Tues 6 Feb	Lecture 22	John	Parsing for Context-free Languages: The CYK Algorithm last year's video playlist, slides
	Thu 8 Feb	Lecture 23	John	LL(1) Predictive Parsing last year's video playlist, slides
5	Tues 13 Feb	~~Lecture 24~~	Mary (CANCELLED due to strike)
5	Thu 15 Feb	~~Lecture 25~~	Mary (CANCELLED due to strike)
FLW	Mon 19 - Fri 23 Feb			NO COURSE ACTIVITIES
6	varies	Tutorial 8	*	Parsing algorithms Solutions, basic slides , Jiaheng's 2023 slides
6	Tues 27 Feb	Lecture 24	Mary	P and NP video playlist (2020/21),, slides	CLRS 34 (intro), 34.1, 34.2, 34.3 OR "Algorithms Illuminated" Chap 19, Sects 22.1, 22.2
7	varies	Lab 9	*	Drop-in for coursework 2
7	Tues 5 Mar	Lecture 25	Mary	Satisfiability and NP-completeness video playlist (2020/21), slides	CLRS 34.4, 34.5 OR "Algorithms Illuminated" Sects 22.3, 22.4, 22.5
8	varies	Lab 10	*	Drop-in for coursework 2
	varies	Tutorial 9	*	NP-completeness ~~and Approximation algorithms~~ Solutions, Jiaheng's 2023 slides
	Tues 12 Mar	Lecture 28	John	Introduction to Computability Theory video playlist (2020/21), slides
9	Tues 20 Mar	Lecture 29	John	Unsolvable Problems video playlist (2020/21), slides
10	varies	Tutorial 10	*	Register machines and computability Solutions
10	Tues 27 Mar	Lecture 30	Mary	Revision Lecture Sample paper and solutions video playlist (from 2020/21), slides

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INF2-IADS: Course Information, Semester 1

flittlet — Tue, 18 Jul 2023 14:49:46 +0000

INF2-IADS: Course Information, Semester 1 flittlet Tue, 18/07/2023 - 15:49

This page contains information relating to lectures, Q&A sessions, tutorials, labs and quizzes in Semester 1. For courseworks, see the Assessment page.

Lectures: In Semester 1, lectures will take place as follows:

Tuesdays (Weeks 1-10) at 10:00-10:50, in Lecture Theatre G03, 50 George Square
Thursdays (Weeks 1-5) at 10:00-10:50, at the locations below:
- Week 1 - EICC Lennox 3
- Week 2 - Online
- Weeks 3-5 - EICC Pentland

Note: The lectures were scheduled to take place initially in Lecture Theatre 5 in Appleton Tower. However, this is one of the venues affected by the RAAC (concrete) issue, so alternative arrangements had to be made.

To view the lectures by livestream, or to watch the recordings later, please use the Lecture Recordings link in Learn; you will need to log in using your EASE account. Slides from each lecture will appear in the Course Schedule.

Pre-recorded video lectures (from previous years):

In the Course Schedule you will also find pre-recorded video lectures from previous years. The pre-recorded lectures cover the same content as the live ones, but with some key differences:

A different lecturer for about half of them (Mary Cryan) rather than the current lecturer (Aris Filos-Ratsikas). So, they are less up-to-date but with a better accent. Don't worry, John is still there for those ones.
They refer to a different textbook (CLRS) rather than the ones that we are using this year (Kleinberg-Tardos and Roughgarden). It is still very much the same material, but the notation and exposition follows CLRS more closely.
They offer a different presentation style, as well as some musical interludes 🎼.

We would recommend that you use these pre-recorded lectures only as an addition (and not as a substitute) to the live ones, as they refer to an older version of the course. Still, they can be very useful!

Tutorials: These will take place in Weeks 3,4,6,8,10 of Semester 1. The problem sheet for each tutorial will be added to the schedule below on the Thursday before the week of the tutorial, and the solutions will be added shortly after the tutorials have taken place.

If you miss your allocated tutorial in a given week, or would just prefer to join a large-group tutorial, a catch-up tutorial. The time and location of the catchup tutorial will be communicated soon.

Lab sessions: In Semester 1, these will be offered as optional drop-in sessions during Weeks 2 and 4 (to provide help with the Python lab sheets) and Weeks 6,7,8 (for help with Coursework 1). All lab sessions will be in Appleton Tower 4.12, with a lab demonstrator present to offer help (the demonstrators will be Year 4 or Year 3 UG students). The times of the sessions during these weeks are as follows:

Monday 11:10-12:00
Monday 16:10 - 17:00
Wednesday 11:10-12:00.
Wednesday 16:10-17:00.

Python lab sheets: These are provided for you to work through at your own pace. Even if you are already fluent in Python, you should take the time to work through these, as the examples are designed to reinforce concepts from the lectures, and there is some important course content relating to data structures within Python itself. We recommend that you work through Sheet 1, Sheet 2, and Sheet 3 in Weeks 1, 2, and 3 respectively.

You may find you are able to work through these entirely by yourself, but if you would like some help with them, this will be available at the lab sessions in Weeks 2 and 4.

Quizzes: There will be six online quizzes throughout the year, three in each semester, to enable you to check whether you're keeping up with the course material. The first quiz is for practice only, the other five carry 2% each of the course mark. These will be online multiple-choice quizzes with 20 questions, to be done in a 1-hour period of your choosing within a 5-day window.

The deadlines for the three Semester 1 quizzes will be as follows:

Quiz 0 (practice only): Monday 9 October [Sem 1 Week 4]
Quiz 1: Monday 30 October [Sem 1 Week 7]
Quiz 2: Monday 27 November [Sem 1 Week 10]

All deadlines are at 12 noon. In each case, we'll release the quiz 5 days before the date shown above. More information on how to access each quiz, and on its possible contents, will be provided when we release it.

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INF2-IADS: Course Information, Semester 2

flittlet — Tue, 18 Jul 2023 14:49:46 +0000

INF2-IADS: Course Information, Semester 2 flittlet Tue, 18/07/2023 - 15:49

This page contains information relating to lectures, Q&A sessions, tutorials, labs and quizzes in Semester 2. For courseworks, see the Assessment page.

Lectures: In Semester 2, lectures will take place as follows:

Tuesdays (Weeks 1-10) at 10:00-10:50, in F21, 7 George Square
Fridays (Weeks 1-5) at 13:10-14:00, in Theatre A, 40 George Square

Pre-recorded video lectures (from previous years):

In the Course Schedule you will also find some pre-recorded video lectures from previous years. In contrast to Semester 1, some of the content for Semester 2 will be different from last year. In particular, several of the applications of greedy algorithms and dynamic programming will be different. Pre-recorded video lectures from last year will only be provided for those lectures that cover the same material. ]

As a rule of thumb: If the lecturer is John, it will be the same as last year. If it is Aris, it will either be a different application altogether (of the same algorithmic techniques), or the same application but with different slides.

We would recommend that you use these pre-recorded lectures only as an addition (and not as a substitute) to the live ones, as they refer to an older version of the course.

Tutorials: These will take place in Weeks 3,4,6,8,10 of Semester 2. The problem sheet for each tutorial will be added to the schedule below on the Thursday before the week of the tutorial, and the solutions will be added shortly after the tutorials have taken place.

If you miss your allocated tutorial in a given week, or would just prefer to join a large-group tutorial, a catch-up tutorial.

Lab sessions: In Semester 2, these will be offered as optional drop-in sessions during Weeks 2,4,6,7, and 8. All lab sessions will be in Appleton Tower 4.12, with a lab demonstrator present to offer help (the demonstrators will be Year 4 or Year 3 UG students). The times of the sessions during these weeks are as follows:

Monday 15:10-16:00
Monday 16:10 - 17:00
Friday 15:10-16:00.
Friday 16:10-17:00.

You may find you are able to work through these entirely by yourself, but if you would like some help with them, this will be available at the lab sessions.

Quizzes: There will be three online quizzes throughout Semester 2 (the first three were in Semester 1), to enable you to check whether you're keeping up with the course material. These will be online multiple-choice quizzes with 20 questions, to be done in a 1-hour period of your choosing within a 5-day window.

The deadlines for the three Semester 1 quizzes will be as follows:

Quiz 3: Monday 5 February [Sem 2 Week 4]
Quiz 4: Monday 4 March [Sem 2 Week 7]
Quiz 5: Monday 3 April [Sem 2 Week 11]

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INF2-IADS: 2023-24

flittlet — Tue, 18 Jul 2023 14:49:45 +0000

INF2-IADS: 2023-24 flittlet Tue, 18/07/2023 - 15:49

Welcome to Informatics 2 - Introduction to Algorithms and Data Structures

It's great to have you on the course this year! We are Aris Filos-Ratsikas and John Longley from the School of Informatics, and we're looking forward to teaching you about Algorithms and Data Structures. We have a large class this year with around 300 students. This OpenCourse site will be the main source for all information and materials relating to the course.

Here's a short video on how to access the material for the course:
Accessing the Materials Video

More information on this year's course will be appearing here in the next few days. Our first lecture is currently scheduled for 10am on Tuesday 19 September, in 50 George Square Lecture Theatre G03.

For Semester 1, lectures are normally scheduled to take place on Tuesdays at 10am in 50 George Square Lecture Theatre G03 (Weeks 1-10), and on Thursdays at 10am in various locations (Weeks 1-5). This is due to the RAAC issue, see the Course Materials for details.

We look forward to seeing you soon anyway!

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