EH315-7-SL-CO:
Advanced Machine Learning: Deep Learning Models
2022/23
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer & Long Vacation
Postgraduate: Level 7
Current
Monday 24 April 2023
Wednesday 04 October 2023
15
03 February 2023
Requisites for this module
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Much of the interesting data in the social sciences consists of text, images, and time series. Think of political speeches, fMRI images, and ERP measures. Our understanding of this data has grown because of advances in machine learning, in particular deep learning. In general terms, machine learning algorithms learn rules or data representations that help us reconstruct outputs from a set of inputs. Deep learning entails the use of successive layers of representations to learn patterns from data. Feed forward, convolutional, recurrent, and other types of artificial neural networks are at the heart of deep learning and are revolutionizing data science. This course is about those algorithms and their uses in image recognition, natural language processing, time series analysis, and unsupervised machine learning. We discuss the theory behind the methods and then use Python and various Python libraries to work through examples of deep learning.
Deep learning algorithms are everywhere--on the Internet, on your cell phone, in business, and in science. These algorithms play a crucial role in the analysis of images, text, video, and time series, among numerous other applications. In this course, you will learn the most fundamental algorithms in the aforementioned areas, to wit autoencoders, convolutional, feed forward, generative adversarial, and recurrent neural networks. You will learn how these algorithms work, how they can be applied, and how they can be programmed using Python. The course is hands-on and problem driven. Using images, text, and time series as the basic data, you will learn how to build appropriate models, program, and evaluate them. Of course, we need to delve into some statistical theory but only to the extent needed to understand the algorithms and their outputs.
At the end of the course, you should have mastered the following milestones:
Understand what deep learning is.
Understand basics of optimization theory, as well as mathematical constructs such as tensors.
Understand how to prepare your computer for deep learning.
Understand the theory behind autoencoders, convolutional, feed forward, generative adversarial, and recurrent neural networks.
Understand how these algorithms can be used to analyse images, text, and time series data.
Know how to program basic algorithms using Python.
By the end of the course, you should understand key algorithms of deep learning, including feed forward, convolutional, recurrent, and generative adversarial networks, as well as autoencoders. You should also be familiar with basic optimization theory and tensors. In addition, you should have developed some hands-on experience in programming in Python, using KERAS and Tensorflow.[1]
[1] Important: This is not a programming course in Python. While you will learn some basic programming concepts, this course does not come close to covering the variety of topics that make up a standard Python programming course.
Prerequisites
This is an advanced course in machine learning. The successful completion of an introductory course in data science, machine learning, or statistical learning is highly recommended. Prior experience with Python is not required. However, prior experience with programming in statistical software like R is recommended.
Representative Background Readings
Chollet, François. 2021. Deep Learning with Python. Manning, 2nd edition. (The course will use this as the background reading.) (this will be provided by ESS)
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
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Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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