AIX0008: AI+X: Introduction to Data Science 2022

General information

Schedule:

	Week 1: June 27- Jul 1
	Monday	Tuesday	Wednesday	Thursday
16:00 - 19:00	Opening ceremony	Lecture 1 Introduction	Lecture 2 Matlab	Lecture 3 Data (1)

	Week 2: Jul 4- Jul 8
	Monday	Tuesday	Wednesday	Thursday
16:00 - 19:00	Lecture 4 Data (2)	Lecture 5 Data Exploration	Lecture 6 k-NN: Regression/ Classification	Lecture 7 Midterm; Market Societies

	Week 3: Jul 11- Jul 15
	Monday	Tuesday	Wednesday	Thursday
16:00 - 19:00	Lecture 8 k-NN: Classification	Lecture 9 Linear Fit	Lecture 10 Clustering	Lecture 11 Neural Network

	Week 4: July 18- July 22
	Monday	Tuesday	Wednesday	Thursday	Friday
16:00 - 19:00	Lecture 12 The machine stops	Lecture 13 Fairness in AI	Lecture 14 Oral presentations	Lecture 15 Oral presentations	Graduation

Overview

Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from possibly noisy, structured and unstructured data, and apply that knowledge across a broad range of application domains. Data science is related to data mining, machine learning, artificial intelligence, and big data. Data science is a term that is meant to unify statistics, data analysis, informatics, and their related methods in order to understand and analyse actual phenomena with data. It uses techniques and theories drawn from mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science.

Highlights:

What is data?
Machine learning: extracting information from data
What is Artificial intelligence?
Ethics: Big Data, Machine Learning, and Society

from Wikimedia

Being responsible for your grades

Grade breakdown

Attendance	5%
Homework	15%
Midterm	20%
Final (paper + oral presentation)	60%

Academic Conduct

The rules for conduct in classes can be summarized with three principles:

Be polite.
Don’t cheat.
Don’t lie.

Be polite

As adults meeting in a professional context, we should all behave professionally: this means being polite and respectful to everyone we deal with.

As the instructor, it is my responsibility to teach as well as I can and to be available, polite and respectful to you.

You are responsible for treating me and your fellow students politely and with respect.

Don’t cheat

As the instructor, it is my responsiblity to make tests and assignments that are fair, to grade fairly, to look for cheating, and to refer students who cheat for possible sanctions.

As students, it is your responsibility to avoid cheating and to discourage other students from cheating.

Don’t lie

Cheating is one form of lies, but there are other. Manipulating data, false claim of ownership of an assignment/idea, plagiarism are all forms of lies. Do not lie to the instructor, and even more importantly, do not lie to yourself!