Learn the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning and deep learning to solve real-life problems.
In this course you will learn the basics of several machine learning topics to help you solve real life challenges. Unsupervised learning techniques such as clustering and dimensionality reduction are useful to make sense of large and/or high dimensional datasets that are not annotated. Deep learning is a supervised learning technique that is useful to train neural networks to solve more complicated classification and regression tasks. Finally, reinforcement learning techniques can be used to train AI agents that interact with an environment.
You will get insight into the fundamental algorithms and basic concepts of:
Clustering is used to identify similar data/objects and patterns from your engineering datasets. It is a technique that is especially useful if you don’t have labeled or annotated data. We explain various approaches to clustering and cover how similarity and dissimilarity measures are used.
Dimensionality reduction techniques are used to reduce the number of features representing a given dataset, while retaining the structure of the dataset. We discuss feature selection and feature extraction techniques such as Principal Component Analysis (PCA), and how and when to apply it.
Deep Learning is a family of machine learning methods based on artificial neural networks. You will learn how to build and train deep neural networks consisting of fully connected neural networks of multiple hidden layers.
Reinforcement learning teaches an AI to interact with an environment. We cover basic reinforcement learning concepts and techniques, such as how to model the system using a Markov Decision Process, and how to train an optimal policy using tabular Q-learning using the Bellman equation.
This course is designed by a team of TU Delft machine learning experts from various backgrounds, highlighting the various topics from their individual perspectives.
What You’ll Learn:
- Describe the main classes of clustering techniques
- Implement k-means and hierarchical clustering
- Motivate the need and choice of dimensionality reduction techniques
- Implement Principal Component Analysis (PCA) for feature extraction
- Explain how deep neural networks work and their advantages
- Train deep neural networks for classification and regression tasks
- Explain the basic concepts and techniques of reinforcement learning
- Describe how reinforcement learning could be applied in real world applications
- Module 0. Welcome to ‘Introduction to Unsupervised, Deep and Reinforcement Learning’
- Module 1. Prior Knowledge: Supervised Machine Learning
- Module 2. Clustering
- Module 3. Dimensionality Reduction
- Module 4. Introduction to Deep Learning
- Module 5. Introduction to Reinforcement Learning
- Module 6. Wrap up
AI Skills: Introduction to Unsupervised, Deep and Reinforcement Learning by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://online-learning.tudelft.nl/courses/ai-skills-introduction-to-unsupervised-deep-and-reinforcement-learning/