LEARNING OUTCOMES
Intended as follow-up for CS-EJ3211 Machine Learning with Python.
Credits: 2
Schedule: 13.09.2021 - 17.12.2021
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Alex Jung, Shamsiiat Abdurakhmanova
Contact information for the course (applies in this implementation):
This is an introductory course where you will learn how to train high-dimensional non-linear models, represented by deep artificial neural networks (ANN), using few lines of Python code. Deep learning is an umbrella term for methods using deep nets, i.e., ANNs that consist of several consecutive layers of artificial neurons. The course gives you a
brief overview of gradient descent which is the most widely used algorithm for tuning the weights of deep nets. You will learn some powerful tricks that allow tuning billions of ANN weights using only hundreds of training examples. Some of the most successful deep learning methods are enabled by few clever regularization techniques, such as data augmentation and transfer learning, to avoid overfitting.
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:
In this course, you will learn how to use state-of-the-art deep learning methods with the programming language Python.
We will discuss key concepts of deep learning, such as artificial neural networks, data augmentation and transfer learning in a hands-on fashion. The grading is based on coding assignments and student projects.applies in this implementation
Round 0. Getting Started with Machine Learning and Python Notebooks.
Round 1. Artificial Neural Networks.
Round 2. Gradient-Based Learning.
Round 3. Convolutional Neural Networks.
Round 4. Regularization.
Round 5. Natural Language Processing.
Assessment Methods and Criteria
applies in this implementation
After successfully completing the course, the student
- understands how ANNs can be used for learning and evaluating high-dimensional non-linear models.
- understands the basic principle of gradient descent.
- is able to build, and train ANNs using the Python package Keras.
- is able to diagnose the learning process by comparing training with validation loss.
- is able to use data augmentation to synthetically enlarge the training set.
- is able to implement transfer learning by fine-tuning a pre-trained deep net.
DETAILS
Study Material
applies in this implementation
Self-contained Python notebooks which include explanations, coding demonstrations and coding assignments.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Period:
2020-2021 Autumn I-II
2021-2022 Autumn I-II
Course Homepage: https://mycourses.aalto.fi/course/search.php?search=CS-EJ3311
Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.