Neural networks have become a cornerstone of modern machine learning, enabling systems to learn from data and solve complex, real-world problems with remarkable accuracy. As the demand for deep learning skills continues to rise across industries, acquiring knowledge of neural network architectures is essential for professionals who want to remain competitive, innovate, and make data-driven decisions.
This eight-week course provides a fundamental understanding of various neural network architectures and their applications in solving complex real-world problems using Python. It specifically addresses the clear industry demand for skills that utilize deep neural networks, including applications such as reinforcement learning, language models, and generative AI. This is relevant even for roles where individuals are not directly building AI infrastructures. Whether you are interested in mastering sequence modeling, improving image recognition, or exploring generative models, this course provides a comprehensive study of cutting-edge techniques in deep learning.
Upon completing this course, you will have a strong knowledge of Neural Networks and the ability to apply these architectures in diverse professional settings. This course forms part of a comprehensive, Machine Learning Practitioner Certificate and provides a key step for anyone looking to harness the power of machine learning and artificial intelligence to drive innovation in their work.
Key benefits
- Tailored for non-technical professionals: Designed specifically for individuals in non-technical roles, this course makes advanced neural network techniques accessible, with minimal assumptions of prior technical expertise.
- Practical applications: Gain hands-on experience, enabling you to immediately apply neural network techniques to industry relevant challenges and from various industries.
- Flexible learning: Learn at your own pace with a mix of asynchronous content and optional live sessions, designed to accommodate your busy schedule.
- Instructor support: Receive expert guidance with access to experienced University of Waterloo faculty and real-time instructor support through live drop-in sessions and online discussion forums.
Learning outcomes:
- Explain the key concepts and architecture of neural networks.
- Explain the fundamental principles of transformers, the intuition behind attention mechanisms, and their applications in various neural network models.
- Apply techniques and strategies to mitigate the implications of algorithmic bias, creating reliable and secure neural network models.
- Apply recurrent neural network and convolutional neural network models to solve relevant problems.
- Describe reinforcement learning principles in real-life scenarios.
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Module 1: Introduction to Neural Networks |
Dive into the fundamentals of neural networks, exploring deep learning concepts, neural network architecture, loss functions, backpropagation, and regularization techniques to optimize model performance and prevent overfitting. |
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Module 2: Recurrent Neural Networks (RNNs) |
Learn about recurrent neural networks (RNNs), focusing on sequence modeling, RNN architecture, gradients, and real-world applications in handling sequence-based data. |
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Module 3: Transformers and Attention |
Discover transformers and attention mechanisms in neural networks, examining how attention improves model performance and exploring applications of transformers in modern AI. |
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Module 4: Convolutional Neural Networks (CNNs) |
Explore convolutional neural networks (CNNs) and their applications in vision-related tasks, including feature extraction through convolution, performance enhancement with pooling and non-linearity layers, and practical CNN use cases across industries. |
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Module 5: Deep Generative Modelling |
Discover deep generative modeling with a focus on generative models and latent variables, auto-encoders, generative adversarial networks (GANs), and the process of training GANs for data generation. |
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Module 6: Robust and Trustworthy AI |
Examine robust and trustworthy AI, focusing on algorithmic bias and debiasing methods, managing uncertainty in deep learning, and addressing challenges in creating reliable AI systems. |
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Module 7: Reinforcement Learning |
Delve into reinforcement learning by exploring Q functions, deep Q networks, policy learning algorithms, and real-life applications in decision-making systems. |
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Module 8: Future Directions |
Explore future directions in neural networks, including emerging trends, challenges, and new opportunities in this rapidly evolving field. |
This course forms part of a comprehensive Machine Learning Practitioner Certificate, and is designed for:
- Individuals in non-technical roles with basic programming and data preparation knowledge, aiming to gain practical, in-demand machine learning and AI skills to stay competitive in the evolving workforce.
- Current or aspiring data analysts or data scientists looking to build a machine learning portfolio.
- Data professionals looking to add machine learning techniques to their domain.
- Programmers and developers looking for a foundational introduction to neural networks, without needing advanced technical preparation, to explore new opportunities in AI and machine learning applications.
Learn alongside your team!
WatSPEED provides custom learning experiences tailored for large groups from any single organization. Register three or more employees from the same organization and receive 15 per cent off. Contact our team at watspeed@uwaterloo.ca for details.
- Exclusive access to your program instructor—a University of Waterloo faculty expert in machine learning.
- Learn at your own pace with weekly independent online learning and hands-on exercises.
- Attend a live orientation session before your course starts to get up to speed on the curriculum.
- Optional live drop-in sessions twice weekly via Zoom where you can ask questions and
receive instructor support on key course concepts:
- Wednesdays, 2 - 2:30 p.m. ET
- Wednesdays, 8 - 8:30 p.m. ET
- Engage directly with your classmates through online discussion boards.
- Approximately five hours of your time each week.
Academic requirements
- Basic coding skills in any programming language are required, or completion of Python for Machine Learning: The Essential Starter Kit (or equivalent knowledge).
- It is recommended that you complete Supervised Machine Learning prior to this course.
- Basic statistics and calculus skills would be an asset, but not required.
System requirements
- Jupyter Notebook/Anaconda (software that you are required to install)
Receive a certificate from the University of Waterloo
Upon successful completion of this program, you will receive a professional education certificate from the University of Waterloo.
This course forms part of a comprehensive, Machine Learning Practitioner Certificate.
Mehrdad Pirnia
Continuing Lecturer, Management Sciences, Faculty of Engineering | Program Author
Dr. Mehrdad Pirnia is a faculty member at the University of Waterloo, Department of Management Sciences. Before joining Waterloo, he worked full-time in California ISO and ALSTOM Grid. He also did an internship at Federal Energy Regulatory Commission (FERC) during his PhD program.
Dr. Pirnia received his Ph.D. degree from University of Waterloo in 2014 in Electrical and Computer Engineering (Power Systems Optimization). The focus of his research is on applying AI, optimization, and stochastic techniques to enhance the operation and planning of energy systems.