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 to 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 with sufficient mathematical background and programming skills (e.g., Bioinformatics, Physics, Mathematics, Statistics, Mathematics-Economics, …) .
The course covers the following tentative topic list:
- Foundations of statistical learning.
- Parametric and non-parametric learning approaches.
- 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.
- Dimensionality reduction and visualization techniques such as principal component analysis (PCA).
At course completion, the successful student will have:
- 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.
- 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.
- 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.
- 7,5 ECTS
- Type of assessment
- Written assignment, 7 daysOne written take-home assignment.
- Exam registration requirements
There are five to seven mandatory written take-home assignments. A student must score above 50% on average in the assignments in order to be eligible for the exam.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
The re-exam is a 7-day written take-home assignment.
Prerequisites for participation in the re-exam are handing in the course assignments no later than 2 weeks prior to the exam and scoring at least 50% on average in these assignments.
Criteria for exam assesment
See Learning Outcome.
- Practical exercises
- Theory exercises
- Exam Preparation