LEARNING OUTCOMES
Understanding of the general principles of deep learning, and the central deep learning methods discussed in the course. After the course, you should be able to apply them to real-world data sets.
Credits: 5
Schedule: 07.01.2025 - 20.03.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Juho Kannala, Jorma Laaksonen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Fundamental and current topics of deep learning. Implementing algorithms on a computer are a part of the course and the programming language is Python. Python-based softwares that allow for symbolic differentiation will also be used.
Assessment Methods and Criteria
valid for whole curriculum period:
Exercises and exam.
Workload
valid for whole curriculum period:
1 lecture per week (1h 30 min total each), one computer exercise session per week (1 h 30 min total each), and the rest for studying the course material, doing exercises and preparing for an exam.
DETAILS
Study Material
valid for whole curriculum period:
Material produced for the course such as lecture slides, external material. The external material will include the 'Deep Learning'-book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, published by MIT Press in 2016 (online version freely available at http://www.deeplearningbook.org), Python material.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring III - IV
2025-2026 Spring III - IV