PALS0039 Introduction to Deep Learning for Speech and Language Processing
UCL Division of Psychology and Language Sciences

Week 1 - What is Deep Learning?

In which we discuss the characteristics of deep learning and consider its relationship with other approaches to machine learning and its position within the field of Artificial Intelligence.

Learning Objectives

By the end of the session the student will be able to:


  1. Motivation for the course
  2. Some demonstrations of how deep learning is creating radically new applications of computer science. We look at IBM's Watson Text-to-Speech system, the use of deep learning in autonomous vehicles, deep reinforcement learning for playing games, generation of images from textual descriptions, neural machine translation, and spoken dialogue systems.

  3. Deep learning, machine learning and artificial intelligence
  4. We position deep learning within the field of machine learning and within the area of artificial intelligence (AI). We give four different ways of thinking about the goals of AI. We contrast AI with the emerging field of computational cognitive science.

  5. Machine learning compared to rule induction and statistical analysis
  6. We introduce the field of machine learning and contrast it with conventional approaches to building intelligent applications. We consider the overlap between machine learning and conventional statistical analysis.

  7. Types of machine learning
  8. We differentiate Supervised, Reinforcement, and Unsupervised approaches to machine learning with some example problems. We also contrast a classification problem from a regression problem.

  9. The deep learning revolution
  10. We introduce the particular characteristics of the "deep" approach to machine learning. We highlight the use of neural networks with large numbers of layers, the application to real-world problems, the reduced reliance on human expertise, the desire for "end-to-end" processing that combines feature estimation with problem learning, and the creation of new abstract data representations. We discuss why deep learning has become important at this point in the history of computer science.

  11. The computational framework for the course
  12. We introduce the Colaboratory environment and the Python programming language that we will be using for demonstrations and exercises through the course.

Web Learning Resources


Be sure to read one or more of these introductions to Machine Learning:

Tutorial Notebooks


Implement answers to the problems described in the notebooks below. Save your completed notebooks into your personal Google Drive account.

    1. Python practice
    2. Numpy and Scipy practice
    3. Matplotlib practice

Word count: . Last modified: 22:45 11-Mar-2022.