• 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 task
• Explain the basic concepts and techniques of reinforcement learning
• Describe how reinforcement learning could be applied in real world applications

Topic 1: Introduction

This is an introduction to the course with an overview of the topics.

Topic 2: Clustering

Clustering techniques are used to identify similar data/objects and patterns from your engineering datasets.

You will learn about the problem of clustering, the main classes of clustering techniques and how we can implement k-means and hierarchical clustering.

• Introduction to clustering

• Formalizing the problem of clustering

• Similarity and dissimilarity measures

• k-means clustering

• Hierarchical clustering

Topic 3: Dimensionality Reduction

Dimensionality reduction techniques are used to reduce the number of features representing a given dataset, while retaining the structure of the dataset as much as possible.

You will learn what dimensionality reduction is, why it is needed and how to use it. You will learn about Principal Component Analysis dimensionality reduction technique and how and when to apply it.

• What is dimensionality reduction?

• Why dimensionality reduction techniques are used

• Feature selection vs. feature extraction techniques

• Principal component analysis feature-extraction technique

Topic 4: Introduction to Deep Learning

Deep learning is a broader family of machine learning methods based on artificial neural networks.

You will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about fully connected neural networks, some theoretical aspects of deep learning, the back-propagation algorithm, Adam, and much more.

• What neural networks are

• Architecture of fully connected neural networks

• Capabilities of neural networks

• The benefits of deep architectures

• Training deep neural networks with stochastic gradient descent and Adam

Topic 5: Introduction to Reinforcement Learning

Reinforcement learning teaches an AI to interact with an environment.

You will be introduced to basic reinforcement learning concepts and techniques, and how they could be applied in real world applications.

• How an AI interacts with the environmen

• The formalization of this as a Markov Decision Process

• An agent’s behavior as a policy

• The optimal policy found using tabular Q-learning

• Why tabular reinforcement learning does not scale well

• Neural networks for deep reinforcement learning to scale up to interesting applications

"Among the most valuable aspects of this course was its comprehensive coverage of cutting-edge AI topics. The deep dive into unsupervised learning, deep learning, and reinforcement learning equipped me with a robust understanding of the core mechanisms driving AI innovations today. What stood out about this course was its relevance and applicability to real-world scenarios. The practical, hands-on approach allowed me to apply theoretical knowledge to actual projects. "

- Tea Larson Hetrick, computer programmer and student from United States

"I enrolled in this course to challenge myself and expand my horizons. It's a step towards fulfilling my aspirations.

The course was a transformative experience that reshaped my career trajectory. It not only deepened my expertise but also broadened my professional network. Learning online was a incredibly flexible and convenient. The ability to access a wealth of resources and interact with peers globally, from the comfort of my home, was truly remarkable"

- Cheerla Balaji Srinivasu, Student from India