Data Analytics and Machine Learning (5 cr)
Code: 3011633-3006
General information
- Enrollment
-
01.12.2023 - 17.01.2024
Registration for the implementation has ended.
- Timing
-
08.01.2024 - 30.04.2024
Implementation has ended.
- Number of ECTS credits allocated
- 5 cr
- Local portion
- 5 cr
- Mode of delivery
- Contact learning
- Unit
- Engineering and Business
- Campus
- Kupittaa Campus
- Teaching languages
- Finnish
- English
- Seats
- 10 - 35
- Degree programmes
- Degree Programme in Business Information Technology
- Teachers
- Golnaz Sahebi
- Matti Kuikka
- Teacher in charge
- Matti Kuikka
- Groups
-
PTIETS22swisPTIETS22 Software Development and Information Systems
- Course
- 3011633
Realization has 6 reservations. Total duration of reservations is 14 h 0 min.
| Time | Topic | Location |
|---|---|---|
|
Tue 02.04.2024 time 14:00 - 15:00 (1 h 0 min) |
Kysely- ja tukitunti, Data-analytiikka ja Koneoppiminen 3011633-3006 |
ICT_B1038
IT telakka
|
|
Fri 05.04.2024 time 09:00 - 12:00 (3 h 0 min) |
Guidance to Team work, Data-analytiikka ja Koneoppiminen 3011633-3006 |
ICT_C3036
Cyberlab / BYOD
|
|
Tue 09.04.2024 time 14:00 - 15:00 (1 h 0 min) |
Kysely- ja tukitunti, Data-analytiikka ja Koneoppiminen 3011633-3006 |
Teams - linkki kurssin ITS:ssä
|
|
Fri 12.04.2024 time 09:00 - 12:00 (3 h 0 min) |
Teori ja käytäntö, Data-analytiikka ja Koneoppiminen 3011633-3006 |
ICT_C3036
Cyberlab / BYOD
|
|
Fri 19.04.2024 time 10:00 - 13:00 (3 h 0 min) |
Demonstrations. Data-analytiikka ja Koneoppiminen 3011633-3006 |
ICT_C2027
IT telakka
|
|
Tue 23.04.2024 time 12:00 - 15:00 (3 h 0 min) |
Team work presentations, Data-analytiikka ja Koneoppiminen 3011633-3006 |
ICT_C2027
IT telakka
|
Evaluation scale
H-5
Content scheduling
Week 2:
- Machine learning landscape (Introduction to machine learning)
Weeks 3 - 7:
- End-to-end machine learning process
Weeks 9-15:
- Presentation of teamwork
- Classification
- Training linear models
- Decision trees
- Unsupervised learning
- Neural networks (Introduction to neural networks)
Week 16: Presentation of project works
Objective
After completing the course the student:
- Can define the main concepts related to data analytics and machine learning
- Understands the value and the drivers for data analytics and machine learning
- Can describe the processes of data analytics and machine learning
- Can use some tools for data analytics and machine learning
Content
Introduction to data analytics and machine learning
Data analytics process and methods
Machine learning process and methods
Practical work
Materials
Mainly according to the book (from chapters 1-10)
[Aurélien Géron] Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Publisher : O'Reilly Media; 2022, 3rd Edition
In addition: Material prepared by the teacher, online material and tasks in the learning environment.
Teaching methods
Lähiopetus, tietokoneavusteinen opetus, tehtäväperustaisuus
Exam schedules
-
International connections
The course includes approximately 12 guided working and theory sessions, 9 personal practice tasks and group work.
*
Group work is done in groups of 3-4 people outside of guidance sessions. The group sets aside 15 minutes outside of guidance sessions to present the group work.
Student workload
Contact hours:
- Course start-up: 2h
- Weeks 3 - 5: Theory & practice (3h/week): 5 x 3h = 15h
- Weeks 9 - 15: Theory & practice (3h/week): 7 x 3h = 21h
- Week 16: Project work presentations: 3h
- In addition, in weeks 4 - 15, about 10 support and inquiry hours: 10 x 1h = 10h
Total contact hours: about 51 hours
Independent study and homework: about 90 h
Further information
The course materials and assignments are at ITS.
The exercises are mainly performed using Jupyter Notebook.
Communication about the course via ITS, but also via the course's Teams channel.