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  • MOOC 
    AI For Everyone

    AI For Everyone

    AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI

    Subjects:
    Computing
    Keywords:
    Artificial intelligence
    Resource Type:
    MOOC
     
  • Presentation 
    Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping

    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
     
    OER
  • Video 
    But what is a Neural Network? | Deep learning, chapter 1

    But what is a Neural Network? | Deep learning, chapter 1

    This video introduces the basic concept and the overall working process of the ANN models, especially how the backpropogation works. How to establish a deep neuro network.

    Course related:
    LGT6801 Guided Study in Logistics I
    Subjects:
    Computing
    Keywords:
    Machine learning Neural networks (Computer science)
    Resource Type:
    Video
     
  • Courseware 
    CS229 - Machine Learning

    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

    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?

    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

    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
     
  • Video 
    Crash Course Artificial Intelligence

    Crash Course Artificial Intelligence

    In 20 episodes, Jabril will teach you about Artificial Intelligence and Machine Learning! This course is based on a university-level curriculum. By the end of the course, you will be able to: * Define, differentiate, and provide examples of Artificial Intelligence and three types of Machine Learning: supervised, unsupervised, and reinforcement * Understand how different AI and ML approaches can be combined to create compelling applications such as natural language processing, robotics, recommender systems, and web search * Implement several types of AI to classify images, generate text from examples, play video games, and recommend content based on past preferences * Understand the causes of algorithmic bias and audit datasets for several of these causes

    Subjects:
    Computing
    Keywords:
    Human-Computer Interaction Machine learning Artificial intelligence
    Resource Type:
    Video
     
  • Video 
    Crash Course Computer Science

    Crash Course Computer Science

    In 40 episodes, Carrie Anne Philbin teaches you computer science! This course is based on introductory college-level material as well as the AP Computer Science Principles guidelines. By the end of this course, you will be able to:

    *Outline the history of computers and the design decisions that gave us modern computers

    *Describe the basic elements of programming and software

    *Identify the basic components of computer hardware and what they do

    *Describe how computers are used and how that has evolved over time

    *Appreciate how far computers have come and how far they might take us

    Course related:
    AMA2222 Principles of Programming
    Subjects:
    Computing
    Keywords:
    Computer science
    Resource Type:
    Video
     
  • MOOC 
    Data Science

    Data Science

    Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers. Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team

    Subjects:
    Computing
    Keywords:
    Machine learning Data mining Big data
    Resource Type:
    MOOC