Fundamentals of Deep Learning (5 ECTS)

Location and time

Course can be done in own pace in TechClass environment.

Materials

Online.

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

Evaluation methods and criteria

Exercises 50%
Quizzes 25%
Project 25%

Student workload

Lectures = 40h
Exercises = 15h
Self-study = 40h
Quizzes = 10h
Project = 30h
Total = 135 hours

Content scheduling

Up to student her-/himself.

Teaching methods

100% online Self-Study course.

- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, satisfactory (1)

- The student knows the basic definitions of Deep Learning.
- The student knows the general difference between Machine Learning and Deep Learning.
- The student knows the primary types of Deep Learning methods.
- The student is familiar with the concept of mapping that learning algorithms perform.
- The student is familiar with variations of Neural Network algorithms used for different applications.

Assessment criteria, good (3)

- The student is familiar with basic concepts of the Neural Network algorithm such as wight and neuron.
- The student knows the reasons for the advent of Deep Learning and its history.
- The student knows the intuitions behind Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- The student is familiar with the steps of training and evaluating Neural Networks.
- The student knows the applications of CNNs and RNNs.

Assessment criteria, excellent (5)

- The student understands the concepts and intuitions behind the Neural Network algorithm.
- The student knows the concepts of regularization, overfitting, and hyperparameters selection.
- The student knows layers of CNNs.
- The student is familiar with the popular architectures of CNNs.