Opinto-opas

Tavoitteet

After completing the course, the student can:
- Can define the main concepts, values and drivers for deep learning
- Can describe how machine learning and AI solutions can be developed with deep learning and neural networks
- Use tools when creating the solutions

Sisältö

Deep Learning
Neural Networks
Natural Language Processing
Pattern Recognition
Computer Vision
Practical work

Opiskelijan ajankäyttö ja kuormitus

12 sessions (2.9-29.11.24 ) each 3 hours (1h lecture, 2h practice)+ Exam

Contact hours:
- Course start-up (week 36): 2h
- Weeks 37 - 48: Theory & practice (3h/week): 12 x 3h = 36h
- Week 49: Exam: 2h
- In addition, about 5 support and inquiry hours (biweekly): 5x 1h = 5h

Total contact hours: 45 hours
Independent study and homework: about 90 h

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

After the course, students should understand the main principles of deep learning and steps needed for applying them in real applications. The student especially learns the core concepts of deep neural networks, gradient descent, model evaluation, overfitting, and underfitting and is able to find a suitable balance between these extremes in a given problem at hand.

This course comprises 100 study points including:
-- 24 points (1+1p each contact class: Lecture and Practical Session)
-- 36 points for exercises
-- 40 points for the exam

-Participation and exercise (50% of total to pass): Students must achieve at least 50% of the points to pass the course. Participation is

- Exam (50% of total points to pass): Students must achieve at least 50% of the points (20 points) in order to pass the course.

The course is graded on a scale of 0-5.

Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-70%
3: 70-80%
4: 80-90%
5: 90- 100%