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Probabilistic Neural Networks and Deep Learning
160 students

Probabilistic Neural Networks and Deep Learning

Probability Theory, Neural Networks, Regression, and Representation Learning
Created byTaman Belajar, Bina Nusantara University
Last updated 10/2025
English

What you'll learn

  • Providing a conceptual and probabilistic foundation for understanding artificial neural networks.
  • Equipping students with an understanding of discrete and continuous probability distributions, as well as parameter estimation techniques for model building.
  • Explains how regression and classification can be modeled with simple (single-layer) networks, including decision theory and regularization.
  • Exploring multilayer architecture and the advantages of deep networks in representation, transfer learning, and error functions for various tasks.

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Course content

4 sections • 12 lectures • 1h 17m total length

  • Overview3:41

    Explore probabilistic foundations of neural networks and deep learning, from probability theory to implementing and evaluating multi-layer perceptrons, CNNs, and RNNs (LSTM/GRU) with standard metrics.

  • Deep Learning Revolution7:14

    Trace the history from machine learning to deep learning, explain artificial neural networks and learning concepts, and explore applications from medical diagnosis to AlphaFold protein structure prediction and synthetic data.

  • Probabilistic (Bayesian & Densities)8:25
  • Probabilistic (Characteristic & Bayesian Link)6:07
  • Foundations of Deep Learning

Requirements

  • A basic understanding of linear algebra, calculus, and probability theory.
  • Basic Python programming skills (familiarity with NumPy, Matplotlib, or similar libraries is helpful).
  • A general understanding of machine learning concepts (optional but beneficial).

Description

Course Probabilistic Foundations of Neural Networks and Deep Learning is designed to equip students with a basic to advanced understanding of the concepts and applications of Deep Learning. In this course, students will learn the probabilistic foundations that underlie machine learning, including probability theory, standard distributions, and parameters. The discussion continues with single-layer networks for regression and classification, which provides insight into how simple models can be linked to probability theory and loss functions.

Next, students will explore deep neural networks with a focus on multilayer perceptron (MLP) architecture, non-linear activation functions, and how network depth increases representation capacity. This course also discusses important issues such as the curse of dimensionality, regularization, and decision theory in making optimal predictions. In the final session, students are introduced to the concepts of representation learning, transfer learning, and various error functions relevant to modern model development.

Through a combination of mathematical and probabilistic theory and practical implementation, this course provides comprehensive skills for understanding, designing, and evaluating artificial neural network architectures. By the end of the course, participants are expected to be able to explain the basic principles of deep learning, implement regression and classification models, and understand the benefits of networks in representation learning and knowledge transfer.

Who this course is for:

  • Students and researchers interested in probabilistic deep learning.
  • Professionals who want to apply neural networks in real-world tasks.
  • Learners with basic math or programming skills.
  • Beginners seeking a clear path from theory to implementation.

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