DS817: Algorithms we live by
Study Board of Science
Teaching language: English
EKA: N340068102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Master
STADS ID (UVA): N340068101
ECTS value: 5
Date of Approval: 31-10-2023
Duration: 1 semester
Version: Approved - active
Comment
Entry requirements
Academic preconditions
Course introduction
In this course we will examine human and artificial intelligence comparatively. We will look at state-of-the-art cognitive models that describe how humans cope with a wide range of demanding tasks. Then we will use that knowledge as a lever to understand machine learning models for dealing with the same problems (or vice versa). The topics discussed will include, among others, categorization and forecasting, recommendation, ranking and search, optimization and planning, creativity, as well as different flavors of multi-armed bandits and reinforcement learning.
We will examine scenarios where humans are assisted by artificial intelligence, and problems where humans compete against algorithms. Further, we will discuss whether human cognitive processes can be recovered from the behavioral traces that people leave behind on the Internet; Finally, we will look at problems where human intelligence can provide insights and inspiration for the development of new methods in the quest for better AI agents.
We will examine scenarios where humans are assisted by artificial intelligence, and problems where humans compete against algorithms. Further, we will discuss whether human cognitive processes can be recovered from the behavioral traces that people leave behind on the Internet; Finally, we will look at problems where human intelligence can provide insights and inspiration for the development of new methods in the quest for better AI agents.
In relation to the competence profile of the degree it is the explicit focus of the course to:
The students will become familiar with seminal algorithms that are deployed in online interfaces and govern our every-day life and will learn how people adapt their decision-making processes to these algorithms. We will discuss problems commonly encountered by start-up companies (e.g. in the e-commerce or media domains) but also by individual decision makers and organizations more broadly.
The acquired competencies will be useful for people planning to work as product managers, data analysts or data scientists in the industry. The course builds and expands knowledge acquired in the core courses of the program, and provides entry level introduction on a number of important topics in data science (i.e. recommender systems, social network analysis, reinforcement learning, machine learning with humans in the loop, natural language processing). Some, of these topics might be revisited in greater depth in other elective topics of the program.
The acquired competencies will be useful for people planning to work as product managers, data analysts or data scientists in the industry. The course builds and expands knowledge acquired in the core courses of the program, and provides entry level introduction on a number of important topics in data science (i.e. recommender systems, social network analysis, reinforcement learning, machine learning with humans in the loop, natural language processing). Some, of these topics might be revisited in greater depth in other elective topics of the program.
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
By the end of the course students are expected to develop a better understanding machine learning algorithms and human cognition. They should be able to have a good grasp of cognitive models that describe human behavior and machine learning models for solving practical problems encountered by individuals and organizations such as categorization, estimation, ranking, clustering etc.
Content
The following main topics are contained in the course:
- The exploration-exploitation trade-off: when should people or algorithms try new things and when should they settle for tested courses of action?
- Supervised learning and the bias/variance dilemma: what are the main approaches in estimation and categorization in machine learning?
- What does it mean for a model to overfit the data?
- What are good ways to get around this problem?
- Unsupervised learning and natural language processing: Can AI learn without a teacher telling it what is right or wrong?
- How can this be achieved?
Humans collaborating and competing with AI:
- What techniques where used in Deep Blue when it won Kasparov?
- What techniques did IBM use to win in Jeopardy?
- Similarly, what type of AI algorithms were used by Deepmind to win human champions in the game of Go?
- How can humans and machines effectively combine their distinct intelligences?
Human behavior and big data:
- What can big data teach us about human behavior?
- How are our data used by companies to personalize their products to us?
Literature
Examination regulations
Exam element a)
Timing
Juni
Tests
Written exam
EKA
N340068102
Assessment
Second examiner: Internal
Grading
7-point grading scale
Identification
Student Identification Card
Language
Normally, the same as teaching language
Duration
2 hours
Examination aids
The exam is without aids. However, standard build in calculator in Windows/MAC are allowed. It is also allowed to use Maple, Mathematica, Mathcad, MathLab, GeoGebra Apps, R, R-Studio, CAS TI-Nspire, Ms Excel og LibreOffice Calc. WordMat is allowed but not recommended. Use of WordMat is at your own risk and no support is provided for errors caused by the program. Furthermore, it is also allowed to use "ordbogsprogrammet" (the dictionary programme) from http://www.ordbogen.com/ in electronic form. The browser version is not allowed.
Internet is not allowed during the exam. However, you may visit system "DE-Digital Exam".
ECTS value
5
Additional information
The re-exam will be oral.
Indicative number of lessons
Teaching Method
The teaching method is based on three phase model.
- Intro and Skills training phase: 12 lectures ( 24 hours in the class)
- 72 preparation for the lectures
- 38 hours preparation for the exams
Study phase activities: Relevant papers and other resources will be highlighted in the syllabus, and the students will be invited to read the before and after the lectures.
Teacher responsible
| Name | Department | |
|---|---|---|
| Pantelis Pipergias Analytis | pantelis@sam.sdu.dk | Strategic Organization Design (SOD) |
Timetable
Administrative Unit
Team at Educational Law & Registration
Offered in
Recommended course of study
Transition rules
Transitional arrangements describe how a course replaces another course when changes are made to the course of study.
If a transitional arrangement has been made for a course, it will be stated in the list.
See transitional arrangements for all courses at the Faculty of Science.