Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

Presentation

Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Comparing frequency counts over texts or corpora is an important task in many applications and scientific disciplines. Given a text corpus, we want to test a hypothesis, such as "word X is frequent", "word X has become more frequent over time", or "word X is more frequent in male than in female speech". For this purpose we need a null model of word frequencies. The commonly used bag-of-words model, which corresponds to a Bernoulli process with fixed parameter, does not account for any structure present in natural languages. Using this model for word frequencies results in large numbers of words being reported as unexpectedly frequent. We address how to take into account the inherent occurrence patterns of words in significance testing of word frequencies. Based on studies of words in two large corpora, we propose two methods for modeling word frequencies that both take into account the occurrence patterns of words and go beyond the bag-of-words assumption. The first method models word frequencies based on the spatial distribution of individual words in the language. The second method is based on bootstrapping and takes into account only word frequency at the text level. The proposed methods are compared to the current gold standard in a series of experiments on both corpora. We find that words obey different spatial patterns in the language, ranging from bursty to non-bursty/uniform, independent of their frequency, showing that the traditional approach leads to many false positives.

Subjects:: Management and Computing
Keywords:: Computational linguistics Text processing (Computer science) Discourse analysis -- Data processing
Resource Type:: Presentation

Courseware

CS229 - Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

Subjects:: Computing
Keywords:: Pattern perception -- Statistical methods Machine learning
Resource Type:: Courseware

Courseware

CS50’s Introduction to Artificial Intelligence with Python

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Course related:: COMP3011 Design and Analysis of Algorithms, COMP1001 Problem Solving Methodology in Information Technology, COMP4434 Artificial Intelligence, and COMP2011 Data Structures
Subjects:: Human-Computer Interaction and Computing
Keywords:: Computer programming Computer science Python (Computer program language) Artificial intelligence
Resource Type:: Courseware

Video

Can robots be creative?

People have been grappling with the question of artificial creativity -- alongside the question of artificial intelligence -- for over 170 years. For instance, could we program machines to create high quality original music? And if we do, is it the machine or the programmer that exhibits creativity? Gil Weinberg investigates this creative conundrum.

Subjects:: Electronic and Information Engineering
Keywords:: Robotics Artificial intelligence
Resource Type:: Video

Others

Codecademy: Learn to Code for Free

Learn to Code for Free. We're here to make coding more accessible, so everyone can learn the skills they need to upgrade their careers. For example, you can learn Python, HTML, CSS, and JavaScript.

Subjects:: Computing
Keywords:: Computer programming Programming languages (Electronic computers)
Resource Type:: Others

MOOC

Data Science for Construction, Architecture and Engineering

The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.

Keywords:: City planning -- Statistical methods Python (Computer program language) Information visualization
Resource Type:: MOOC

Others

Deep Learning

Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.

Subjects:: Computing
Keywords:: Python (Computer program language) Machine learning
Resource Type:: Others

Video

DigitalSreeni Youtube Channel

This channel walks you through the entire process of learning to code in Python; all the way from basics to advanced machine learning and deep learning. The primary emphasis will be on image processing and other relevant functionality. Why did I create this channel? To help you (students and researchers) gain a new skill and succeed in your respective fields.
You may think coding is hard and that it's not your cup of tea, but Python made it easy to code even advanced algorithms. In addition, coding will make you self sufficient, it will teach you how to think, it improves your collaborative skills and it can take your career to new heights. Therefore, if you want to stay ahead of your peers and relevant in your field, overcome your fears and start coding!