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# FYS-STK3155/4155 Applied Data Analysis and Machine Learning, http://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/index-eng.html
This site contains all material relevant for the course on Data Analysis and Machine Learning FYS-STK3155/4155.
This site contains all material relevant for the course on Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway.
## Introduction
Probability theory and statistical methods play a central role in science. Nowadays we are
surrounded by huge amounts of data. For example, there are about one trillion web pages; more than one
hour of video is uploaded to YouTube every second, amounting to 10 years of content every
hour of video is uploaded to YouTube every second, amounting to years of content every
day; the genomes of 1000s of people, each of which has a length of more than a billion base pairs, have
been sequenced by various labs and so on.
This deluge of data calls for automated methods of data analysis,
which is exactly what machine
learning provides.
been sequenced by various labs and so on. This deluge of data calls for automated methods of data analysis,
which is exactly what machine learning provides.
## Learning outcomes
The course introduces a variety of central algorithms and methods
essential for studies of data analysis and machine learning. The course is project based and through the various projects, normally three, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
This course aims thus at discussing many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. You will learn to develop and structure large codes for studying different systems where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
- Learn about basic data analysis, statistical analysis, Monte Carlo sampling, data optimization and machine learning;
- Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
- Be capable of extending the acquired knowledge to other systems and cases;
- Have an understanding of central algorithms used in data analysis and machine learning;
- Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications;
@@ -28,11 +25,11 @@ essential for studies of data analysis and machine learning. The course is proje
- Learn about about decision trees and random forests
- Learn about support vector machines and kernel transformations
- Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
- Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
- Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++.
## Prerequisites
Basic knowledge in programming and mathematics, with an emphasis on linear algebra. Knowledge of Python or/and C++ as programming languages is required and experience with Jupiter notebook is recommended. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100.
Basic knowledge in programming and mathematics, with an emphasis on linear algebra. Knowledge of Python or/and C++ as programming languages is strongly recommended and experience with Jupiter notebook is recommended. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities offer nowadays a basic programming course (often compulsory) where Python is the recurring programming language.
## The course has two central parts
@@ -40,6 +37,8 @@ Basic knowledge in programming and mathematics, with an emphasis on linear algeb
1. Statistical analysis and optimization of data
2. Machine learning
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions where say elements from statistical data analysis are mixed with specific Machine Learning algorithms.
### Statistical analysis and optimization of data
The following topics will be covered
@@ -65,9 +64,9 @@ The following topics will be covered
All the above topics will be supported by examples, hands-on exercises and project work.
Computational aspects play a central role and the students are
Computational aspects play a central role and you are
expected to work on numerical examples and projects which illustrate
the theory and methods. Some of the projects can be coordinated with the high-performance programming course IN4200.
the theory and methods. We recommend strongly to form small projects of 2-3 participants. Some of the projects can be coordinated with the high-performance programming course IN4200.
@@ -79,7 +78,9 @@ the theory and methods. Some of the projects can be coordinated with the high-pe
4. A selected number of weekly assignments;
6. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
7. Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail;
8. The course will be offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course.
8. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
9. We use Piazza for course communication, a special link on how to register to Piazza con be found on the official University of Oslo page for the course. Slack is also used for course communication;
## Possible textbooks