Introduction to Practical Machine Learning, 2 credits
Introduktion till praktisk maskininlärning, 2 hp
TDDE77
Main field of study
Information Technology Computer Science and Engineering Computer ScienceCourse level
First cycleCourse type
Programme courseDirector of studies or equivalent
Jolanta PielaszkiewiczEducation components
Preliminary scheduled hours: 0 hRecommended self-study hours: 53 h
Course offered for | Semester | Period | Timetable module | Language | Campus | ECV | |
---|---|---|---|---|---|---|---|
6CDDD | Computer Science and Engineering, Master of Science in Engineering | 6 (Spring 2026) | 1 | 1 | Linköping, Valla | C |
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
First cycleAdvancement level
G2FCourse offered for
- Master of Science in Computer Science and Engineering
Prerequisites
Mathematical analysis, linear algebra, statistics, programming in Python.
Intended learning outcomes
The overall aim of the course is to provide an introduction to machine learning with a focus on implementing and using models based on neural networks. The course will provide skills in implementing basic deep learning models in a dedicated software library.
After completion of the course, the student should be able to:
1. Use basic concepts and methods in machine learning to formulate, structure, and solve practical problems.
2. Construct basic models for classification and regression.
3. Implement basic neural network-based machine learning models in Python using a deep learning framework and fit these to training data.
4. Use pre-trained machine learning models and analyze their performance.
Course content
This course offers a gentle introduction to machine learning with a focus on implementing and using models based on neural networks. This includes: unsupervised and supervised learning; regression and classification; model training, selection, and evaluation; neural networks; convolutional neural network; deep learning; machine learning operations; application of the methods to real data. The course will provide skills in implementing basic deep learning models in a dedicated software environment, including support for GPU acceleration and automatic dierentiation, which are central
features for practical implementation of deep neural networks.
Teaching and working methods
The teaching consists of lectures and computer labs. Lectures are used to introduce basic concepts and theory that the students then use in practical problem solving within the computer labs. The computer labs also introduce the students to a deep learning framework (e.g., PyTorch).
Examination
LAB1 | Laboratory work | 2 credits | U, G |
LAB1 consists of laboratory assignments that test the students' ability to solve practical machine learning problems.
Grade for examination module is decided in accordance with the assessment criteria presented at the start of the course.
Grades
Two-grade scale, U, GDepartment
Institutionen för datavetenskapCode | Name | Scope | Grading scale |
---|---|---|---|
LAB1 | Laboratory work | 2 credits | U, G |
LAB1 consists of laboratory assignments that test the students' ability to solve practical machine learning problems.
Grade for examination module is decided in accordance with the assessment criteria presented at the start of the course.
Note: The course matrix might contain more information in Swedish.
I | U | A | Modules | Comment | ||
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1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
1.1 Knowledge of underlying mathematics and science (courses on G1X-level) |
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X
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LAB1
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1.2 Fundamental engineering knowledge (courses on G1X-level) |
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X
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LAB1
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1.3 Further knowledge, methods and tools in any of : mathematics, natural sciences, engineering (courses at G2X level) |
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X
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X
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LAB1
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1.4 Advanced knowledge, methods and tools in any of: mathematics, natural sciences, engineering (courses at A1X level) |
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1.5 Insight into current research and development work |
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2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
2.1 Analytical reasoning and problem solving |
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X
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X
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LAB1
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2.2 Experimentation, investigation, and knowledge discovery |
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X
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X
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LAB1
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2.3 System thinking |
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2.4 Attitudes, thought, and learning |
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2.5 Ethics, equity, and other responsibilities |
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3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
3.1 Teamwork |
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X
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LAB1
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3.2 Communications |
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X
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LAB1
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3.3 Communication in foreign languages |
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X
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LAB1
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4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
4.1 Societal conditions, including economically, socially and ecologically sustainable development |
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4.2 Enterprise and business context |
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4.3 Conceiving, system engineering and management |
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4.4 Designing |
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4.5 Implementing |
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X
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LAB1
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4.6 Operating |
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5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
5.1 Societal conditions, including economically, socially and ecologically sustainable development within research or development projects |
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5.2 Economic conditions for research or development projects |
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5.3 Identification of needs, structuring and planning of research or development projects |
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5.4 Execution of research or development projects |
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5.5 Presentation and evaluation of research or development projects |
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