Elements of Machine Learning (EML)
Course content
Machine learning lies at the heart of Artificial Intelligence. This course considers machine learning at an advanced step of the data processing pipeline, where it is used to turn data into knowledge.
Students will be introduced to the basics of machine learning including foundations, deep learning, writing code for machine learning in practice, modern machine learning tools, libraries & infrastructures.
The course covers the following tentative topic list:
- Foundations of learning and generalisation;
- Non-linear classification;
- Non-linear regression;
- Neural networks and deep learning.
BSc Programme in Computer Science
At course completion, the successful student will have:
Knowledge of
- The general principles of machine learning;
- Basic concepts underlying classification and regression;
- Neural networks and deep learning;
- Common pitfalls in machine learning.
Skills in
- Applying nonlinear techniques for classification and regression;
- Writing code for machine learning in practice;
- Visualising 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
- Recognising 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.
Lecture and exercise classes.
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 the undergraduate
courses (e.g. NMAB15002U Lineær Algebra i Datalogi) in the computer
science education at the University of Copenhagen 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 corresponding
to the undergraduate courses (e.g., NDAB18002U Matematisk Analyse
og Sandsynlighedsteori i Datalogi and NDAB16012U Modelling and
Analysis of Data) in the computer science education at the
University of Copenhagen is expected (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. We recommend the first four chapters of
"Probability and Computing" by Mitzenmacher and
Upfal.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Continuous assessmentContinuous assessment based on 4-6 take-home assignments. The final grade will be a weighted average over all assignments.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 18
- Theory exercises
- 36
- Practical exercises
- 36
- Exam
- 88
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAB18003U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
- Duration
-
1 block
- Schedulegroup
-
B
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Jens Petersen (4-776f7c77476b7035727c356b72)
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Courseinformation of students