NDAK15007U Machine Learning (ML)
MSc Programme in Computer Science
MSc Programme in Bioinformatics
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, Mathematics-Economics, …) with sufficient
mathematical background and programming skills.
The course covers the following tentative topic list:
- Foundations of statistical learning.
- Likelihood framework, parametric and non-parametric representations.
- Classification methods, such as: Linear models, K-Nearest Neighbor, kernel-based methods (e.g., support vector machines), and neural networks.
- Regression methods, such as: Linear regression, non-linear regression.
- Clustering.
- Dimensionality reduction and visualization techniques such as principal component analysis (PCA).
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 non-linear 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 real-world data mining and pattern recognition problems by using machine learning techniques.
See Absalon when the course is set up.
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 high-school 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 1-3.4, 4.1, 5-6.4, 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.
- Category
- Hours
- Lectures
- 28
- Practical exercises
- 57
- Preparation
- 14
- Project work
- 50
- Theory exercises
- 57
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
- Credit
- 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 take-home assignment.
- Exam registration requirements
There are five to seven mandatory written take-home assignments (which may include programming tasks), all but one of which must be passed in order to be eligible for the exam.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
If a student is not qualified then qualification can be achieved by hand-in and approval of equivalent assignments.
The reexam is a written take-home assignment.
Criteria for exam assesment
See learning outcome.
Course information
- Language
- English
- Course code
- NDAK15007U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Course responsibles
- Yevgeny Seldin (6-84767d757a7f51757a3f7c863f757c)
Lecturers
Christian Igel