Machine Learning (ML)
Course content
The amount and complexity of available data is steadily
increasing. To make use of this wealth of information, computing
systems are needed that turn the data into knowledge. Machine
learning is about developing algorithms for analysing data for
making predictions, categorizations, and recommendations. Machine
learning algorithms are already an integral part of today's
computing systems  for example in search engines, recommender
systems, or biometrical applications. Machine learning
provides a set of tools that are widely applicable for data
analysis within a diverse set of problem domains such as data
mining, search engines, digital image and signal analysis, natural
language modeling, bioinformatics, physics, economics, biology,
etc.
The purpose of the course is to introduce students the basic theory
and most common techniques of statistical machine learning. The
students will obtain a working knowledge in statistical machine
learning.
This course is relevant for computer science students as well as
for students from others studies (e.g., Bioinformatics, Physics,
Mathematics, Statistics, MathematicsEconomics, …) with sufficient
mathematical background and programming skills.
The course covers the following tentative topic list:
 Foundations of statistical learning.
 Likelihood framework, parametric and nonparametric representations.
 Classification methods, such as: Linear models, KNearest Neighbor, kernelbased methods (e.g., support vector machines), and neural networks.
 Regression methods, such as: Linear regression, nonlinear regression.
 Clustering.
 Dimensionality reduction and visualization techniques such as principal component analysis (PCA).
MSc Programme in Computer Science
MSc Programme in Bioinformatics
At course completion, the successful student will have:
Knowledge of
 the general principles of machine learning;
 basic probability theory for modeling and analyzing data;
 the theoretical concepts underlying classification, regression, and clustering;
 the mathematical foundations of selected machine learning algorithms;
 common pitfalls in machine learning.
Skills in
 applying linear and nonlinear techniques for classification and regression;
 performing elementary dimensionality reduction;
 elementary data clustering;
 implementing selected machine learning algorithms;
 visualizing and evaluating results obtained with machine learning techniques;
 using software libraries for solving machine learning problems;
 identifying and handling common pitfalls in machine learning.
Competences in
 recognizing and describing possible applications of machine learning;
 comparing, appraising and selecting machine learning methods for specific tasks;
 solving realworld data mining and pattern recognition problems by using machine learning techniques.
Lecture and exercise classes.
See Absalon when the course is set up.
Knowledge of and experience in programming is required.
Participants must be able to implement algorithms described in
pseudo code.
Knowledge of linear algebra corresponding to an introductory
undergraduate course on the topic is expected (in particular:
vector spaces; matrix inversion; eigenvalue decomposition; linear
projections). This knowledge can be acquired/refreshed using any
introductory book on linear algebra (e.g., Gilbert Strang,
"Introduction to Linear Algebra").
Knowledge of basic calculus at an advanced highschool level is
also expected (in particular: rules of differentiation; simple
integration). This knowledge can be acquired/refreshed using any
introductory book on calculus (e.g., Stephen Abbott,
"Understanding Analysis"; Michael Spivak, "The
Hitchhiker's Guide to Calculus"). There is a free online
textbook and course "Calculus" by Gilbert Strang
available at MIT OpenCourseWare,
http://ocw.mit.edu . The most
relevant chapters/sections in this book are 13.4, 4.1, 56.4, 10,
11, and 13.
Knowledge of basic statistics and probability theory is a plus (in
particular: discrete and continuous random variables; independence
of random variables and conditional distributions; expectation and
variance of random variables; central limit theorem and the law of
large numbers). This knowledge can be acquired/refreshed using any
introductory book on these topics (e.g., Sheldon Ross, "A
First Course on Probability Theory", in particular the first
six chapters). There is a free online course "Introduction to
Probability and Statistics" by Jeremy Orloff and Jonathan
Bloom available at MIT OpenCourseWare,
http://ocw.mit.edu , in particular
the first part "Probability" is relevant.
Participants with weaknesses in one or more of the above areas
should be prepared to spend some extra study time on their own,
either before or during the course.
This course was formerly known as "Statistical Methods for Machine Learning". The replacement course for nonComputer Science students is called "Data Analysis Methods".
 ECTS
 7,5 ECTS
 Type of assessment

Written assignment, due on the last day of the block. The students have seven days to work on the exam.One written takehome assignment.
 Aid
 All aids allowed
 Marking scale
 7point grading scale
 Censorship form
 External censorship
Criteria for exam assessment
See learning outcome.
Single subject courses (day)
 Category
 Hours
 Lectures
 28
 Preparation
 14
 Practical exercises
 57
 Theory exercises
 57
 Project work
 50
 English
 206
Kursusinformation
 Language
 English
 Course number
 NDAK15007U
 ECTS
 7,5 ECTS
 Programme level
 Full Degree Master
 Duration

1 block
 Schedulegroup

C
 Capacity
 No limit
 Studyboard
 Study Board of Mathematics and Computer Science
 Department of Computer Science
Course responsible
 Yevgeny Seldin (684767d757a7f51757a3f7c863f757c)
Teacher
Christian Igel
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