dipopotamus/custom_instruct_coursera_course_module_list huggingface dataset:
Course: Machine Learning: Concepts and Applications
Modules:
Course Introduction, The Data Science Pipeline, Data Ingestion and Exploration, Lab Walkthrough: Data Exploration with Pandas, Supervised Learning, Linear Models, and Least Squares, Lab Walkthrough: Linear Regression
Linear Regression and Least Squares, Lab Walkthrough: Linear Regression on the Prostate Cancer Dataset, Maximum Likelihood Estimation, Lab Walkthrough: Linear Regression and Maximum Likelihood Estimation
Basis Functions, Lab Walkthrough: Features and Basis Functions, Regularization and the Bias-Variance Tradeoff, Lab Walkthrough: Linear Regression: Regularization
Model Selection and Cross Validation, Lab Walkthrough: Model Selection and Pipelines, Logistic Regression, Lab Walkthrough: Logistic Regression
Support Vector Machines, Lab Walkthrough: Support Vector Machines, Naive Bayes Classification, Naive Bayes Classification Example
Tree-Based Models, Ensembles, Bagging, and Boosting, Lab Walkthrough: Trees and Forests, Evaluation Metrics, Lab Walkthrough: Evaluation
Unsupervised Learning (K-Means, Hierarchical), Lab Walkthrough: Clustering, Clustering (KDE, Meanshift, DBSCAN), Lab Walkthrough: Density and Distribution-Based Clustering
Principal Component Analysis (PCA), Lab Walkthrough: Principal Component Analysis, Temporal Models and Hidden Markov Models, Lab Walkthrough: Hidden Markov Models
Feed-Forward Neural Networks, Lab Walkthrough: Feed Forward Neural Networks, Convolutional Neural Networks, Lab Walkthrough: Convolutional Neural Nets
Course: Sustainable Cities Case Studies
Modules:
Meet the Instructor: William Shutkin, Meet the Instructor: Shane Casey
Welcome to Module 1, 2023 Maui Fire, Urban Firestorm, Firewise Community
Welcome to Module 2, Transit-Oriented Development, Green Gentrification, Green Jobs Transition
Welcome to Module 3, Climate Action Plan, Bicycle Infrastructure, Energy Transition, Flood Resilience
Welcome to Module 4, Urban Agriculture, Water Management, Circular Economy
Course: Equilibrio, ¿por qué se caen las cosas?
Modules:
Introducción, Leyes de Newton, Escalares y vectores, Vectores, Equilibrio de partículas, resortes, y roce, 1.6 Apoyos y vínculos, diagramas de cuerpo libre y equilibrio de sistemas de partículas
Momento, Problemas 2D con momento, Problemas 2D con momento, Problema en 3D, Problema 3D con momento
Resultantes en 2D, Resultantes en 2D, Resultantes en 3D, Resultantes en 3D, Resultantes en 3D
Fuerzas distribuidas, Fuerzas distribuidas, Ejercicio resuelto 1, Ejercicio resuelto 2, Centroides, Ejercicio resuelto, Centroides, Ejercicio resuelto 1, Ejercicio resuelto 2, Ejercicio resuelto 1, Ejercicio resuelto 2, Presiones hidrostáticas
Roce, Ejercicio resuelto, Diagramas de cuerpo libre y equilibrio en 2D, Ejercicio resuelto 1, Ejercicio resuelto 2, Diagramas de cuerpo libre y equilibrio en 2D, Ejercicio resuelto 1, Ejercicio resuelto 2, Diagramas de cuerpo libre y equilibrio en 3D, Diagramas de cuerpo libre y equilibrio en 3D, Ejercicio resuelto
Reticulados, Reticulado - Método de los nodos, Reticulado - Método de las secciones, Estructuras de marco, Estructuras de marco
Course: Introduction to Social Determinants of Health
Modules:
Introduction to Social Determinants of Health: Data to Action Specialization, Introduction to Course 1: Introduction to the Social Determinants of Health, Introduction to Social Determinants of Health, Data, Power, and Partnership
Systems Thinking: The Distinctions, Systems, Relationships, Perspectives (DSRP) Theory, The Social Determinants of Health Data to Action Hourglass Model
Data Driven Collective Impact
Minority Stress Theory Part 1, Minority Stress Theory Part 2
Introduction to Frequency Analysis and Bar Chart Visualization, Introduction to the R Environment in Coursera, Introduction to the Data Files, Frequency Analysis and Bar Chart Visualization
Course: Potenciando mi aprendizaje en el primer año de Universidad
Modules:
¡Bienvenidos!, Autoconocimiento, motivación y aprendizaje activo.
Estrategias de estudio asociadas a las clases.
Estrategias de estudio asociadas a las horas de trabajo autónomo I.
Estrategias de estudio asociadas a las horas de trabajo autónomo II.
Estrategias de estudio asociadas a las instancias de evaluación I.
Estrategias de estudio asociadas a las instancias de evaluación II.
Estrategias para manejar las emociones frente a las instancias de evaluación.
Oportunidad para mejorar mis estrategias de estudio.
Manejo del tiempo.
Condiciones para el aprendizaje.
Course: How to Get Into AI
Modules:
Welcome to the course
Interview with an AI professional: Dr Ricardo Anderson, Interview with an AI professional: María Constanza Molina Cortes Funes
Course: The Good, Bad, and Ugly of Marketing - Jagdish Sheth
Modules:
Course Intro, The Good Marketing: How to Achieve It - 01, The Good Marketing: How to Achieve It - 02, The Good Marketing: How to Achieve It - 03, The Good Marketing: How to Achieve It - 04, The Good Marketing: How to Achieve It - 05, The Good Marketing: How to Achieve It - 06, The Good Marketing: How to Achieve It - 07, The Good Marketing: How to Achieve It - 08, The Good Marketing: How to Achieve It - 09
Does Stakeholder Marketing Pay Off? - 01, Does Stakeholder Marketing Pay Off? - 02, Does Stakeholder Marketing Pay Off? - 03, Does Stakeholder Marketing Pay Off? - 04, Does Stakeholder Marketing Pay Off? - 05, Does Stakeholder Marketing Pay Off? - 06, Does Stakeholder Marketing Pay Off? - 07, Does Stakeholder Marketing Pay Off? - 08, Does Stakeholder Marketing Pay Off? - 09, Does Stakeholder Marketing Pay Off? - 10, Does Stakeholder Marketing Pay Off? - 11
Course: Generative AI in User Research and Design Thinking
Modules:
Course Introduction, Design Thinking: Overview, Introduction to using AI in User Research​, Crafting Effective Prompts: Strategies, Crafting Effective Prompts: Demo, Setting the Course: Pre-Discovery in Design Research, Using Mind Mapping to Identify Gaps, Using AI tools for Mind Mapping  - Demo
Overview of Secondary Research​, AI Powered Document Analysis, Using AI in Competitive Analysis, Welcome to Primary Research, Introduction to Primary Research, Collating Secondary Research Findings: A Step-by-Step Guide, Collating Secondary Research Findings - Demo, Preparing a Discussion Guide, Preparing a Discussion Guide  - Demo
Synthesis - Navigating User Research Insights, Data Collection for Synthesis - Demo, Building Affinity Maps using AI - Demo, Generating Insights, Create User Journeys with GPTs, Creating User Journeys with GPT - Demo, Wrapping Up
Course: Explainable Machine Learning (XAI)
Modules:
The Black Box: Motivation for XAI, A Good Decision, Defining Interpretability, Explainability, and Transparency, Introduction to Local Explanations, LIME, Anchors, Shapley Values, SHAP, Individual Conditional Expectation (ICE) Plots, Introduction to Global Explanations, Functional Decomposition, Feature Interaction, Permutation Feature Importance, Partial Dependence Plots, Accumulated Local Effects (ALE) Plots, Introduction to Example-based Explanations, Prototype-based Explanations, Counterfactual Explanations, Influential Instances
Feature Visualization, Feature Attribution, Network Dissection, Concept Activation Vectors, A Review of Attention, Visualizing Attention, Interpretable Attention: The Debate, Saliency Methods as Alternatives
XAI in LLM Challenges, XAI in LLM Fine-tuning, XAI in LLM Prompting, XAI in Knowledge Augmentation (RAG), XAI in Generative Computer Vision, XAI in GANs, XAI in Diffusion Models
Course: Test-Driven Development Overview
Modules:
Course Introduction, Origins and Terms, Benefits of Using TDD, Why adopt TDD?, Solutions for TDD Adopters, Organization-wide Benefits, Module 1 Wrap Up
How do we use TDD to create clean code?, The TDD Mantra, A narrated and animated view of the workflow., A Day in the Life, Introduction to Lab Exercises, Module 2 Wrap Up
Existing Types of Testing - Where does TDD Fit?, Module 3 Wrap Up
Using the Exercises, Wrap Up
Course: Health Impacts of Chemicals in Consumer Products
Modules:
Meet Your Instructors, Welcome to the Course, Assessment Framework - Part A Importance of exposure, Assessment Framework - Part B Needs and assessment framework, Which method for which problem? When to use Risk Assessment (RA), Chemical Alternatives Assessment (CAA)and Life Cycle Assessment (LCA)? use RA, CAA, and LCA - Part A, Which method for which problem? Description of RA, CAA, and LCA - Part B, Personal Care Products (PCPs): Part A1a Defining product intake fraction (PiF) and compartment of entry, PCPs - Parabens: Part A1b Mass balance calculation of the PiF, Impacts of chemicals in personal care products - Parabens exposures, Parabens - impacts and risks, Impacts of chemicals in PCPs - Overview human health impacts & risks of formaldehyde in PCP, Introduction to USEtox near-field - formaldehyde in shampoo, USEtox –The Product Interfaces – Exposures, risks and damages metrics, USEtox – The UNEP-SETAC toxicity model The Personal Care Product Interface – Exposures, risks and damages, User input data for the Personal Care Product Interface - USEtox – The UNEP-SETAC toxicity model, [Optional] USEtox transfers and product intake fractions  (optional for the general version of USEtox without using product interface), [Optional]USEtox dose and risks: the Personal Care Product interface  (optional for the general version of USEtox without using product interface), [Optional] USEtox impacts (optional for the general version of USEtox without using product interface)
Impacts of PCPs - Should we care - PCP in history and regulations, Impacts of chemicals in products - Should we care - PCPs chemicals of concern and mitigation strategies, Impacts and risks of chemicals in household products - Exposure & impacts per chemical-product combination, Impacts and risks of chemicals in household products - Household products and chemicals of concern: impacts, Impacts and risks of chemicals in household products - Household products and chemicals of concern: risks, Chemicals in toys Part A Amounts of chemicals, Chemicals in toys - Part B Exposure, Chemicals in  toys - Part C Risks and chemicals of high concern, Risks of chemicals in toys - Mouthing and chemicals of concern in toys and buildings, The Toy Interface - Exposures, risks and damages
Introduction to Chemical Alternatives Assessment (CAA), CAA - steps and needs for life cycle based substitution, Framework for a Life Cycle Based alternatives Assessment, LCAA - Flooring case study, USEtox –The Building Materials Interface  – Exposures, risks and damages, Impacts of fine particulate and chemicals in products, Impacts of fine particulates on human health - Framework, Impacts of fine particulates on human health - Exposure, Impacts of fine particulates on human health - exposure-response, Impacts of fine particulates on human health - impacts of a computer & IO, Exposome, Products, and Health - Part A, Exposome, Products, and Health - Part B, Summary - Health Impacts of Chemicals in Consumer Products, End Of Series Wrap Up
Course: Supply Chain Management: A Learning Perspective
Modules:
Foundation of SCM Implementation Part 1, Foundation of SCM Implementation Part 2, Learning from History,i.e., Experience Part 1, Learning from History,i.e., Experience Part 2, Learning from History,i.e., Experience Part 3
What is Supply Chain Management, Value Creation Hierarchy, What is Value Chain, Focal Company in Supply Chain Management, Value Life Cycle Part 1, Value Life Cycle Part 2
Management Capability, Controllability versus Flexibility Part 1, Controllability versus Flexibility Part 2, Tradeoff between Capabilities Part 1, Tradeoff between Capabilities Part 2, Integrating Capability, Dynamic Changes of Capability, Chain of Capability, Incremental versus Radical Improvement
Learning Capability - Basic Definition, Learning Capability - Dynamic Perspective, Learning Capability - An Example I, Learning Capability - An Example II, Learning Capability - An Example III, Cause-and-Effect Analysis, Single-loop and Double-loop Learning, Learning Dynamics - Learning Propensity Model, Learning Organization for SCM
Quality - Multidimensional Nature, Quality - Order Winning versus Qualifying, Quality Costs - Quality Failure, Total Quality Management, Quality Improvement Dynamics - Quality is Free?, Quality Dynamics - "Quality-Learning", Quality - Statistical Process Control Part 1, Quality - Statistical Process Control Part 2, Quality - Process Capability
New Product Innovation, NPD Process - Two Approaches Part 1, NPD Process - Two Approaches Part 2, Cost-Benefit Analysis, Important Role of CFT Approach, Three Stages of Change
Formal Definition of SCM, Designing Factors - Structural Dimension Part 1, Designing Factors - Structural Dimension Part 2, Designing Factors - Infrastructural Dimension, Sustainable SC Coordination, SCM Process Strategy, To Minimize the Mismatch between Supply and Demand
Supply Chain Coordination, Bullwhip Effect and Information Quality, Vendor-Managed Inventory (VMI), Postponement
Global Supply Chain Management Part 1, Global Supply Chain Management Part 2, Value Chain Sustainability, Effective Decision Making for Global, Sustainable SCM, Managerial Insights
Final Exam
Course: Basics of Inclusive Design for Online Education
Modules:
Introduction to Course, Introduction to Course Logistics, Three Factors of Course Accessibility, Demographics and Inclusive Design, Student Perspectives, Introduction to Universal Design with Sheryl Burgstahler
A Review of Online Accessibility Issues, Creating Accessible Microsoft Word Documents, Creating Accessible Adobe PDF Documents from Word, Creating Accessible Microsoft Powerpoint Documents
Complex Images, Tables, Graphs, Alternate Text for Images & Tables, Describing Complex Images
Introduction to Captioning, UD Benefits of Captioning, DIY Captioning with Amara - part 1, DIY Captioning with Amara - part 2, Captioning with YouTube - part 1, Captioning with YouTube - part 2
Keyboard Accessibility, Color Contrast, Lecture: Interview on Learning Management Systems with Hadi Rangin, Resources for Accessibility and Universal Design
Course: Использование языка Python для доступа к веб-данным
Modules:
Добро пожаловать на курс, Добро пожаловать в Python — Гвидо ван Россум, Windows 10: установка Python и написание программы, Windows: снимки экрана, Macintosh: использование Python и написание программы, Macintosh: снимки экрана
11.1. Регулярные выражения, 11.2. Извлечение данных, Бонус: Академические часы... в Гааге, Бонусное интервью: Бьярне Строуструп — C++
12.1. Сетевые технологии, 12.2. Протокол передачи гипертекста (HTTP), Пример с решением: сети и сокеты (глава 12), Использование консоли разработчика для изучения HTTP, Бонус: Леонард Клейнрок — первые два пакета в ARPANET, Бонусное видео: Роберт Кайо — соавтор Всемирной паутины, Бонус: Академические часы... в Атланте, Джорджия (Бакхед), Минутка отдыха: доктор Чак читает новости на CNN
12.3. Символы и строки Юникода, 12.4. Получение веб-страниц, Пример с решением: использование Urllib (глава 12), 12.5. Парсинг веб-страниц, Пример с решением: BeautifulSoup (глава 12), Бонус: Академические часы... в Монреале, Бонусное интервью: Тим Бернерс-Ли - изобретение Всемирной паутины, Минутка отдыха: I Got My Mojo Working — Женева, Швейцария
13.1. Данные в Интернете, 13.2. Расширяемый язык разметки (XML), 13.3. XML-схема, 13.4. Парсинг XML, Пример с решением: XML (глава 13), Интервью: Рой Филдинг — принципы работы архитектуры REST, Бонус: Академические часы... в Бостоне, Бонусное видео: Ян Хоррокс — RDF/OWL (продвинутый уровень)
13.5. JavaScript Object Notation (JSON), Пример с решением: JSON (глава 13), Интервью: Дуглас Крокфорд — знакомство с JSON, 13.6. Сервис-ориентированный подход, Видео: сервис-ориентированные архитектуры, 13.7. Использование интерфейсов прикладного программирования, Пример с решением: GeoJSON API (глава 13), 13.8. Защита запросов API, Пример с решением: API Twitter (глава 13), Бонус: Академические часы... в Мельбурне, Австралия, Бонус: Академические часы... в Санта-Монике, Калифорния, Бонус: встреча выпускников в Блетчли-парке
Course: Low Intermediate English: Personal Growth & Well-Being
Modules:
Welcome to the Low Intermediate English Specialization, Introduction to Voxy, Tips for Taking the VPA, Introduction to Personal Growth & Well-Being
Introduction to Healthy Habits
Introduction to Personal Growth
Introduction to Workplace Well-Being
Introduction to Professional Development
End-of-Course Project Overview, Study Tip: Practice Regularly to Maintain and Improve Your Proficiency Level, Congratulations on Completing Personal Growth & Well-Being!, Congratulations on Completing the Low Intermediate English Specialization
Course: Mindfulness in Integrative Healthcare
Modules:
Introduction to this Course, What Is Mindfulness--and What Isn't It?, Ways to Practice, Mindfulness in Daily Life, Try These Daily Life Practices, Why Meditate?, How to Practice Mindfulness Meditation, Guided Meditation - 5-Minute Awareness of Breath, What is the State of the Evidence?, How Mindfulness Works, Benefits of Mindfulness
Mindfulness for Pain Management, Lisa Uses Mindfulness to Help with Chronic Pain, Guided Meditation - Being Mindful of Pain, Mindfulness for Anxiety and Depression, Guided Meditation - Being Mindful of Anxiety, Mindfulness for Wellbeing, Guided Meditation - Being Mindful of Wellbeing, Cautions and Contraindications
Making the Recommendation, Addressing Challenges, Starting with Daily Life Practices, Introducing the STOP Technique, Watching Your Speech, Using Guided Meditation, 10-Minute Guided Meditation: Awareness of Body and Breath
Peer Project Overview
Mindfulness in the Workplace, How Providers Benefit from Mindfulness, Ideas for Practice from Healthcare Providers, Guided Meditation - Working with Residue, Evaluating the Impact, Call to Action!
Course: Linux for Developers
Modules:
Introduction to Linux for Developers (Course 2), Module 1 Overview, Kernel vs. Operating System and Tools, History of Linux, UNIX and Linux, Linux Distributions, Using Linux Distributions (Demo), Graphical Layers and Interfaces, Using KDE Desktop (Demo), Getting Help, Graphical Interfaces: GNOME and KDE
Module 2 Overview, Introduction to Text Editors, vi Editor, emacs Editor, The Development of Shells, Introduction to Filesystems, Using fdisk (Demo), Partitioning Considerations
Module 3 Overview, System Boot, Using GRUB (Demo), System Initialization, Using Swap and OOM (Demo), Threading Models, Using Predictable Network Interface Device Names
Module 4 Overview, File Transfer Tools, Graphical Monitoring Tools, Using System Monitoring (Demo), Using Kernel Modules (Demo), Device Management, Using udev (Demo), Using systemctl (Demo)
Module 5 Overview, System Installation, Using Graphical Package Management (Demo), Using Yast (Demo), Using rpm (Demo), Using dpkg (Demo), Using dnf (Demo), Basics of Users and Groups, Creating, Modifying, and Removing User Accounts (Demo)
Course: Hands-on Text Mining and Analytics
Modules:
1.1 Description of the course including the objectives and outcomes, 1.2 Explanations of the y-TextMiner package and the datasets, 1.3 How-to-do: workspace installation and setup, 1.4 How-to-use: the y-TextMiner package (download it at http://informatics.yonsei.ac.kr/yTextMiner/yTextMiner1.2.zip)
2.1 Description of possible project ideas, 2.2 What is text mining?, 2.3 Description of preprocessing techniques, 2.4 How-to-do: normalization including tokenization and lemmatization, 2.5 How-to-do: N-Grams
3.1 Description of stopword removal, stemming, and POS tagging, 3.2 Explanations of named entity recognition, 3.3 Explanations of dependency parsing, 3.4 How-to-do: stopword removal and stemming, 3.5 How-to-do: NER and POS Tagging, 3.6 How-to-do: constituency and dependency parsing
4.1 Explanations of TF*IDF, 4.2 Explanations of document classification, 4.3 Explanations of sentiment analysis, 4.4 How-to-do: computation of tf*idf weighting, 4.5 How-to-do: classification with Logistic Regression
5.1 Explanations of sentiment analysis with supervised learning, 5.2 Explanations of sentiment analysis with unsupervised learning, 5.3 Explanations of sentiment analysis with CoreNLP, LingPipe and SentiWordNet, 5.4 How-to-do: sentiment analysis with CoreNLP, 5.5 How-to-do: sentiment analysis with LingPipe, 5.6 How-to-do: sentiment analysis with SentiWordNet
6.1 Description of Topic Modeling, 6.2 Explanations of LDA and DMR, 6.3 Description of Topic Modeling with Mallet, 6.4 How-to-do: LDA, 6.5 How-to-do: DMR
Course: Fundraising and Development Foundations
Modules:
Course Introduction Foundations of Fundraising and Development, History and Impact of Philanthropy in the US, Terminology of Fundraising and Philanthropy, Ethics and Guiding Principles of Confidentiality, The Donor and Development Process (Part 1), The Donor and Development Process (Part 2), Campaigns and Opportunities
Introduction to Case for Support, Prospect Identification, and Annual Giving, Case for Support (Part 1), Case for Support (Part 2), Prospect Identification & Building Constituents (Part 1), Prospect Identification & Building Constituents (Part 2), The Annual Fund: Building a Base of Support (Part 1), The Annual Fund: Building a Base of Support (Part 2)
Introduction to Major Gift, Planned Giving, Foundations and Corporate Giving Programs, Major Gift Development Programs (Part 1), Major Gift Development Programs (Part 2), Major Gift Development Programs (Part 3), Planned Giving Programs (Part 1), Planned Giving Programs (Part 2), Foundation and Corporate Giving (Part 1), Foundation and Corporate Giving (Part 2)
Introduction to Donor Relations and Fundraising Campaigns, Stewardship and Recognition Programs (Part 1), Stewardship and Recognition Programs (Part 2), Campaigns: Overview & Pre-planning (Part 1), Campaigns: Overview & Pre-planning (Part 2), Campaigns: Opening & Closing a Campaign (Part 1), Campaigns: Opening & Closing a Campaign (Part 2), Foundations of Fundraising and Development Course Summary
Course: Estructura de capital y política de dividendos
Modules:
Bienvenida
Capital, Financiamiento, Funciones del administrador financiero
Teorema de Modigliani y Miller, Costo Promedio Ponderado de Capital
Beta y retorno esperado
Modelo y fórmula CAPM
Tipos de acciones, Crecimiento de dividendos
Course: Design Thinking for the Greater Good: Innovation in the Social Sector
Modules:
Welcome to the Course!, What is Design Thinking?, Why is Design Thinking Important?, How is Design Thinking Different?, Four Questions Animation, Four Questions and What Is?, Four Questions: What If? What Wows? What Works?, Kingwood Intro, Kingwood and What Is?, Kingwood and What If, What Wows, What Works, Dealing with Complex Social Systems, Visualization, Six Abilities That Matter Most
Week 2 Overview, Introduction to What Is?, Kingwood Trust: Ethnography Part 1, Kingwood Trust: Ethnography Part 2, Monash University Medical Centre Intro, Monash Looks at What Is, Journey Maps and Unmet Customer Needs, Four Things to Know About Journey Maps, Journey Map Example: The Whole Aquarium, More Journey Map Examples, Journey Maps: My Favorite Tool, Assessing and Expanding Your Repertoire
Week 3 Overview, Introduction to What If?, The Challenge of Possibility: Geoffrey and George, Part 1, The Challenge of Possibility: Geoffrey and George, Part 2, Intro to Iveragh, Kerry Part 1, Iveragh and What If, Kerry Part 2, The FDA Intro, FDA Design Thinking Description, Stakeholder Mapping, Stakeholder Example, Storytelling
Week 4 Overview, Intro to What Wows: Assumption Testing, Whiteriver Part 1: First Assumptions, Whiteriver Part 2: Reassessing Assumptions, Intro to What Wows: Prototyping, MasAgro: Creating a Space for Experimentation, MasAgro's Hub System, MasAgro's Innovation Network, Intro to What Works, Monash Learning Launch, Monash Initiative: Long Patient Stays, Integrating DT Across Monash Organization, Reviewing Our Lessons, Actions for Impact
Course: Nearest Neighbor Collaborative Filtering
Modules:
Course Introduction
User-User Collaborative Filtering, Configuring User-User Collaborative Filtering, Influence Limiting and Attack Resistance; Interview with Paul Resnick, Trust-Based Recommendation; Interview with Jen Golbeck, Impact of Bad Ratings; Interview with Dan Cosley
Assignment Introduction, Programming Assignment - Programming User-User Collaborative Filtering
Introduction to Item-Item Collaborative Filtering, Item-Item Algorithm, Item-Item on Unary Data, Item-Item Hybrids and Extensions, Strengths and Weaknesses of Item-Item Collaborative Filtering, Interview with Brad Miller
Item-Based CF Assignment Intro Video, Programming Assignment - Programming Item-Item Collaborative Filtering
The Cold Start Problem, Recommending for Groups:  Interview with Anthony Jameson, Threat Models, Explanations, Explanations, Part II:  Interview with Nava Tintarev
Course: 5. アセット、脅威、そして脆弱性
Modules:
コース 5 へようこそ, ダクイシア：サイバーセキュリティへの道のり, 1 週目へようこそ, アセットのセキュリティにおける What・Why・How, トリ：アセットのセキュリティと私, セキュリティはアセットの分類から始まる, デジタル アセットとデータの状態, セキュリティ計画を構成する要素, NIST サイバーセキュリティ フレームワークとコア機能, まとめ
2 週目へようこそ, セキュリティ コントロールとは, ヘザー：PII を保護することの重要性, 暗号技術の基礎, PKI（公開鍵基盤）, 否認防止とハッシュ化, アクセス制御と認証システム, 認可のメカニズム, ユーザー アクティビティの監視の必要性, ティム：アセットを保護する使命, まとめ
3 週目へようこそ, 脆弱性管理とは, 多層防御とは, 一般的な脆弱性とエクスポージャー, 脆弱性診断とは, オマッド：サイバーセキュリティの学びの旅, すべてのエントリー ポイントを守る, ニル：攻撃者のマインドセットを知る, 防御の経路, まとめ
4 週目へようこそ, 人をだます犯罪のテクニック, 情報のフィッシング, 悪意のあるソフトウェア, クリプト ジャッキングの増加, クロス サイト スクリプティング（XSS）, データベースの隙をつくエクスプロイト, セキュリティへの積極的アプローチ, シャンタル：サイバーセキュリティにおける多様性の価値, PASTA の脅威モデリング, まとめ, コースのまとめ
Course: Understanding Clinical Research: Behind the Statistics
Modules:
Introduction to Understanding Clinical Research, About the course, Observing and intervening: Observational & experimental studies, Observing and describing: Case series studies, Comparing groups: Case-control studies, Collecting data at one point in time: Cross-sectional studies, Studying a group with common traits: Cohort studies, Let's intervene: Experimental studies, Working with existing research: Meta-analysis and Systematic Review, Doing a literature search: Part 1, Doing a literature search: Part 2
Introduction, Some key concepts: Definitions, Data types, Arbitary classification: Nominal categorical data, Natural ordering of attributes: Ordinal categorical data, Measurements and numbers: Numerical data types, How to tell the difference: Discrete and continuous variables, Introduction, Measures of central tendency, Measures of dispersion, (Optional) Setting up spreadsheets to do your own analysis, (Optional) Descriptive statistics using spreadsheets, Making inferences: Sampling, Types of sampling, Case study 1
P-values: P is for probability, Working out the probability: Rolling dice, Area under the curve: Continuous data types, Introduction to the central limit theorem: The heart of probability theory, Asymmetry and peakedness: Skewness and Kurtosis, Learning from the lotto: Combinations, Approximating a bell-shaped curve: The central limit theorem, Patterns in the data: Distributions, The bell-shaped curve: Normal distribution, Plotting a sample statistic: Sampling distribution, Standard normal distribution: Z distribution, Estimating population parameters: t-distribution, (Optional) Generating random data point values using spreadsheet software, Case study 2
Introduction to Hypothesis Testing, Testing assumptions: Null and alternative hypothesis, Is there a difference?: Alternative Hypothesis, Type I and II: Hypothesis testing errors, Introduction to confidence intervals, How confident are you?: Confidence levels, Interval estimation: Confidence intervals, (Optional) Calculating confidence intervals using spreadsheet software
Introduction to parametric tests, Student's t-test, ANOVA, Linear Regression, (Optional) Student's t-test in action, Introduction to nonparametric tests, Checking for normality, Thinking nonparametrically, Comparing paired observations: Signs, Ordering values: Ranking, Paired comparisons: Sign ranks, Summation of ranks: Rank sums, Comparing two populations: Mann-Whitney-U test, More nonparametric tests, Case study 3
Introduction to comparing categorical data, Observed frequencies: Contingency tables, Comparing observed and expected values: Chi-square test, Association between two variables: Fisher's exact test, (Optional) Calculating chi-square test using spreadsheet software, Introduction to sensitivity and specificity, Measuring performance: Sensitivity and specificity, Proportions of results: Positive and negative predictive values, Introdution to risk and odds ratios, Risk and odds ratios - Losses (Risk), Risk and odds ratios - Losses (Odds), Risk and odds ratios - Wins, Risk and odds ratios example
Course: Securing Investment Returns in the Long Run
Modules:
Why you should choose this course, Some common mistakes you will no longer make after this course - Performance, Some common mistakes you will no longer make after this course - Selling, What is useful to know before this course?, Some products you will master when you next meet your bank advisor - Mutual funds, Some products you will master when you next meet your bank advisor - Closed-end funds, Introduction to active versus passive management - Definitions, Introduction to active versus passive management - Passive, Introduction to active versus passive management - Active
What you will learn in this Module, Choosing the right benchmark - UBS guest speaker, Shifting from relative to absolute return mandates, The Sharpe, Treynor and Sortino ratios - Sharpe, The Sharpe, Treynor and Sortino ratios - Treynor and Sortino, Tracking Error - UBS guest speaker, Using asset pricing models to estimate fund performance, Defining performance analysis - UBS guest speaker, Performance attribution - Introduction, Performance attribution - Example
What you will learn in this module, Dimensions in ETF selection - UBS guest speaker, Beating the benchmark or the peer group? - Peer group analysis, Beating the benchmark or the peer group? - Screening criteria, The information ratio says it all, What are the pros and cons of a structured product?, Searching for hedge fund alpha: lessons from academia - Asset pricing model, Searching for hedge fund alpha: lessons from academia - Evidence, Do active funds really outperform passive funds? - Preliminaries, Do active funds really outperform passive funds? - Evidence, Active versus passive management - UNIGE & UBS Discussion - Part 1, Active versus passive management - UNIGE & UBS Discussion - Part 2
What you will learn in this Module, Corporate sustainability and sustainable investing, Sustainable investing: exclusion, impact investing & integration - UBS guest speaker, Does sustainable investing perform in line with traditional investing? - Theoretical arguments, Does sustainable investing perform in line with traditional investing? - Evidence, Climate change and capital markets - Introduction, Climate change and capital markets - Methodology, What is Neurofinance?, Understanding brain activity for better investment decisions, Can business analytics (Big Data) improve the value-added of TAA?, Using a Robo-advisor to better shape the SAA, Specialization Outro
Course: Transacting on the Blockchain
Modules:
Course Introduction, Instructor Introduction, Module 1 Overview, Cryptocurrencies, Protocol Tokens, Non-Fungible Tokens (NFTs), Stablecoins, Securities Tokens, Governance Tokens, Exchange Tokens, Natural Asset Tokens, Central Bank Digital Currencies (CBDCs), Module 1 Recap, Digital Wallets (Optional)
Module 2 Overview, What Are Smart Contracts?, Practitioner Perspective - Rolf Hoefer: Smart Contracts, Smart Contract Phases, Smart vs. Traditional Contracts, Smart Contracts and Law, Practitioner Perspective - Andreas Wallendahl: Smart Contracts, Smart Contract Application Areas, Practitioner Perspective: Rob Carter, Executive VP and CIO, FedEx, Smart Contract Strategies & Best Practices for the Organization, Recap of Module 2
Module 3 Overview, Introduction to Identity and Identifiers, Five Problems With Identifiers, Distributed, Self-sovereign Identity Systems, Practitioner Perspective - Carlos Augier: Identity, Blockchain Identity Applications, Practitioner Perspective - Stephen Tse & Li Jiang: Personal Data, Managing Health Data on a Blockchain, Recap of Identity
Module 4 Overview, Six Inefficiencies in Financial Services, The Golden Eight Part 1, The Golden Eight Part 2, Problems With Modern Accounting, The World Wide Ledger, Recap of Financial Services, Course Wrap-up
Course: Selenium WebDriver with Python
Modules:
Course Overview, Selenium WebDriver Introduction, Differences between Selenium WebDriver and Other Selenium Tools, Installing Python and Pip, Setting up the Environment Part 1 - Demo, Setting up the Environment Part 2 - Demo, Setting up the Environment Part 3 - Demo
Locating Web Elements, Locating Elements using ID, Name, Class, and Tag, Web Interaction, Executing JavaScript Code Part 1, Executing JavaScript Code Part 2, Handling Drop-down Part 1, Handling Drop-down Part 2, Handling Web Elements Part 1, Handling Web Elements Part 2, Navigating to Web Page, Uploading and Downloading with Selenium Part 1, Uploading and Downloading with Selenium Part 2
Testing with Selenium WebDriver, Writing Assertions and Verifications with Test Reporting and Logging, Setting-up Test Cases with Selenium using Python, Handling Exceptions in Selenium Test Using Python, Selenium Web Driver with Python, Using Page Object Model (POM), Handling Pop-ups and Alerts Part 1 - Demo, Handling Pop-ups and Alerts Part 2 - Demo, Handling Frames in Selenium Part 1, Handling Frames in Selenium Part 2, Handling Frames in Selenium Part 3, Best Practices & Tips Overview, Best Practices for Selenium WebDriver Automation Part 1, Best Practices for Selenium WebDriver Automation Part 2, Cross-Browser Testing with Selenium WebDriver Overview, Debugging with Selenium WebDriver
Course: Regression Analysis
Modules:
Introduction to Regression and Linear Regression
Polynomial Regression
Regularization
Cross Validation
Ensemble Methods
Regression Analysis Case Study - Demo, Regression Analysis Case Study
Course: Introduction to Image Generation - Bahasa Indonesia
Modules:
Pengantar Pembuatan Gambar
Course: Cryptocurrency and Blockchain: An Introduction to Digital Currencies
Modules:
Introduction and Summary, Introduction to Cryptocurrency, Transacting in Bitcoin, Why Cryptocurrency?
Introduction to Module 2, The Digital Signature, A Tamper Proof Ledger, What is Blockchain?, Examples, Distributed Consensus, Proof of Work, Mining and Currency Supply, Future Challenges
Cryptocurrency as an Asset Class, Risk and Return to Cryptocurrency, Review of Portfolio Theory, Asset Allocation with Cryptocurrency, Conclusion
Module Introduction, What is an Ecosystem, Building the Blockchain, Crypto Finance, Business Use Cases, Blockchain in Gaming, Investing in Blockchain, Government and Regulation, Media and Advocacy, Creating the New Frontier of FinTech - Securitize, Creating the New Frontier of FinTech - Broadridge Financial Solutions, Inc.
Course: Developing Interpersonal Skills
Modules:
Interpersonal skills: An overview, Let’s begin with the first topic, How might these communication styles come into play?, Recap, Let’s move to the second topic, How can you ensure your messages don’t get lost in translation?, The art of listening, What types of questions can you ask?, Communication channels, Remember, Recap, Let’s move to the third topic, What does assertiveness mean?, Recap, Let’s move to the fourth topic, Perspective-taking, Recap, Let’s move to the fifth topic, Workplace conflicts are opportunities to develop interpersonal skills, Importance of feedback, Recap, Let’s move to the sixth topic, Which is the best way to deal with disputes?, Recap
Course: Gemini for end-to-end SDLC - Deutsch
Modules:
Die Zukunft der Softwareentwicklung mit Gemini
Course: Basic System Programming on IBM Z
Modules:
System Programming, JES and JCL Part 1, JES and JCL Part 2, How to submit and view JCL output, Lab Familiarization Video Part 1, Lab Familiarization Video Part 2, JCL procedures, JCL tidbits
VSAM, z/OS Components, z/OS System Libraries, Application Infrastructure - part 1, Application Infrastructure - part 2, Language Environment, Generation data groups, RAIM, Networking, Utilities
Introduction to z/OSMF, z/OSMF Exercise Introduction, UNIX System Services, USS File System, ISHELL and hierarchical file system exercise introduction, USS processes and permissions, Final words
Course: Statistical Molecular Thermodynamics
Modules:
Video 1.0 - The Thermite Reaction, Video 1.1 - That Thermite Reaction, Video 1.2 - Benchmarking Thermoliteracy, Video 1.3 - Quantization of Energy, Video 1.4 - The Hydrogen Chloride Cannon, Video 1.5 - Atomic Energy Levels, Video 1.6 - Diatomic Molecular Energy Levels, Video 1.7 - Polyatomic Molecular Energy Levels, Video 1.8 - Review of Module 1
Video 2.1 - Ideal Gas Equation of State, Video 2.2 - Non-ideal Gas Equations of State, Video 2.3 - Gas-Liquid PV Diagrams, Video 2.4 - Law of Corresponding States, Video 2.5 - Virial Equation of State, Video 2.6 - Molecular Interactions, Video 2.7 - Other Intermolecular Potentials, Video 2.8 - Review of Module 2
Video 3.1 - Boltzmann Probability, Video 3.2 - Boltzmann Population, Video 3.3 - Ideal Gas Internal Energy, Video 3.4 - Ideal Gas Equation of State Redux, Video 3.5 - van der Waals Equation of State Redux, Video 3.6 - The Ensemble Partition Function, Video 3.7 - The Molecular Partition Function, Video 3.8 - Review of Module 3
Video 4.1 - Ideal Monatomic Gas: qtrans, Video 4.2 - Ideal Monatomic Gas: Q, Video 4.3 - Ideal Monatomic Gas: Properties, Video 4.4 - Ideal Diatomic Gas: Part 1, Video 4.5 - Ideal Diatomic Gas: Part 2, Video 4.6 - Ideal Diatomic Gas: Q, Video 4.7 - Ideal Polyatomic Gases: Part 1, Video 4.8 - Ideal Polyatomic Gases: Part 2, Video 4.9 - Review of Module 4
Video 5.1 - First Law of Thermodynamics, Video 5.2 - Paths of PV Work, Video 5.3 - Differentials and State Functions, Video 5.4 - Characteristic Ideal Gas Expansion Paths, Video 5.5 - Adiabatic Processes, Video 5.6 - Microscopic Origin of Pressure, Video 5.7 - Enthalpy, Video 5.8 - Heat Capacities, Video 5.9 - Thermochemistry, Video 5.10 - Standard Enthalpy, Video 5.11 - Review of Module 5
Video 6.1 - Entropy, Video 6.2 - Entropy as a State Function, Video 6.3 - Spontaneity and the Second Law, Video 6.4 - Statistical Entropy, Video 6.5 - Computing Entropy, Video 6.6 - Entropy and the Partition Function, Video 6.7 - Beta and Boltzmann’s Constant, Video 6.8 - The Carnot Cycle, Video 6.9 - Review of Module 6
Video 7.1 - Entropy and Other Thermodynamic Functions, Video 7.2 - Third Law of Thermodynamics, Video 7.3 - Standard Entropy, Video 7.4 - Entropy from the Partition Function, Video 7.5 - Third Law Entropies, Video 7.6 - Additivity of Entropies, Video 7.7 - Review of Module 7
Video 8.1 - Helmholtz Free Energy, Video 8.2 - Gibbs Free Energy, Video 8.3 - Maxwell Relations from A, Video 8.4 - Maxwell Relations from G, Video 8.5 - Rubber Band Thermodynamics, Video 8.6 - Natural Independent Variables, Video 8.7 - P and T Dependence of G, Video 8.8 - Review of Module 8, Video 8.9 - Credits
Final Exam
Course: Story and Narrative Development for Video Games
Modules:
Welcome, What makes a story compelling?, How games benefit from stories, How to see a game as a story first, Defining the Spine, Three-Act Structure and Rising Action
Writing Your Own Story Synopsis, Defining Characters and Character Goals, Using Secondary Characters, Writing a Character Brief
Story Ideation Techniques, Personal Writing Advice, Refining the Spine, Connecting Ideas and Characters
Introduction to a Game Design Document, Diving Deeper into the Design Document, Turning The Shooter's Story into a Game, The Shooter's Rising Gameplay, Course Wrap Up
Course: Comportamento e Marketing Digital
Modules:
Segmentação de Mercado no Ambiente Digital, Processo Decisório On-line, Vieses Cognitivos no Ambiente Digital, O Consumidor 5.0, Jornada do Cliente On-line, Como ter Sucesso no Ambiente Digital
Marketing e a Estratégia do Negócio, Digital como Mentalidade, Digital como Estratégia, Quando Usar o Marketing Digital, Quando Não Usar o Marketing Digital
Marketing Integrado: Preço, Marketing Integrado: Distribuição, Marketing Integrado: Desenvolvimento de Produtos, Marketing Integrado: Comunicação Integrada de Marketing, Combinando On-line e Off-line
Alinhando Público e Objetivos, O que é o ICP – Ideal Consumer Profile?, Como Usar o ICP em Sua Estratégia, Criando Mais de Um ICP, Testando seu ICP
Course: Data Science as a Field
Modules:
Data Science as a Field Course Introduction, Where Does Data Science Come From?, The Current State of the Field, Where is Data Science Going?
Introduction to "Data Science in Business, Industry, and the Professional World", Brian Brown & Rinaldo Maldera, Natalie Jackson, Vilja Hulden, Robin Burke, Seth Spielman, Katharina Kann, Dan Larremore
Importance and Process of Reproducibility, Knit to PDF, Intro to R Markdown, Overview of Steps in the Data Science Process, Importing Data, Tidying and Transforming Data, Visualizing Data, Analyzing Data, Modeling Data, Bias sources, Intro to Data Ethics course with Bobby Schnabel
Do’s and Don’ts for Good Reports and Presentations, CU Boulder’s MS in Data Science: Where to Go from Here?
Course: Modern Regression Analysis in R
Modules:
Frameworks and Goals of Statistical Modeling, The Assumption of Concept Validity, The Linear Regression Model, Matrix Representation of the Linear Regression Model, Assumptions of Linear Regression, The Appropriateness of Linear Regression, Interpreting the Linear Regression Model I, Interpreting the Linear Regression Model II
Introduction to Least Squares, Linear Algebra for Least Squares, Deriving the Least Squares Solution, Regression Modeling in R: a First Pass, Justifying Least Squares: the Gauss-Markov Theorem and Maximum Likelihood Estimation, Sums of Squares and Estimating the Error Variance, The Coefficient of Determination, The Problem of Non-identifiabiliity, Regression Modeling in R: a Second Pass
Motivating Statistical Inference in the Linear Regression Context, The Sampling Distribution of the Least Squares Estimator, T-Tests for Individual Regression Parameters, T-Tests in R, Motivating the F-Test: Multiple Statistical Comparisons, The F-Test, The F-Test in R, Confidence Intervals in the Regression ContextConfidence Intervals in the Regression Context
Differentiating Prediction and Explanation, Point Estimates for Prediction, Interval Estimates for Prediction, Making Predictions Using Real Data in R, When Prediction Goes Wrong, Defining Causality
Linear Regression Diagnostic Methods, Violations of the Linearity Assumption, Violations of the Independence Assumption, Violations of the Constant Variance Assumption, Violations of the Normality Assumption, Diagnostics in R
Motivating Model Selection Methods, Testing-Based Procedures and their Shortfalls, Criterion-Based Procedures: AIC, Criterion-Based Procedures: BIC, Criterion-Based Procedures: Adjusted R-Squared, The Mean Squared Prediction Error as a Model Selection Method, Model Selection in R, The Problem of Collinearity, Diagnosing Multicollinearity, The Problem of Multicollinearity: Solutions and R Implementation
Course: Welcome to Game Theory
Modules:
Course Preview, 1-1 What is Game Theory?, 1-2 Modelling Social Problems as a "Game", 1-3 In Search for the Governing Principle, 1-4 Concerns About a Mathematical Theory of Human Behavior, 1-5 Let's Play a Game, Card Game Tutorial (No Audio), 1-6 John Nash Discovered the Governing Principle, 1-7 Nash Equilibrium, 1-8 Traffic Game in Reality, 1-9 Location Game, 1-10 Policies of Two Parties
2-1 Nash Equilibrium and the Prisoner’s Dilemma, 2-2 Coordination Game and Self-Fulfilling Prophecy, 2-3 Market Competition, 2-4 Why Do People Come to Play Nash Equilibrium? Part I, 2-5 Why Do People Come to Play Nash Equilibrium? Part II, 2-6 Why Do People Come to Play Nash Equilibrium? Part III, 2-7 Stylized Facts and Nash Equilibrium, 2-8 Make Yourself Unpredictable: Mixed Strategy Equilibrium, 2-9 Sports Games and Game Theory, 2-10 Nash Equilibrium Exists in All Games
3-1 Digression: The Card Game Revisited, 3-2 Digression: How You Played the Card Game and Addressing the Concerns about Game Theory, 3-3 “Payoffs” in a Game: What Exactly Are Those Numbers?, 3-4 What Does it Mean That a Player is Rational?, 3-5 Domination: Strategies That Are “Obviously Good or Bad”, 3-6 Common Knowledge of Rationality, 3-7 Low Rationality: What Happens if Players Are Not Very Smart?, 3-8 Game Theory Under Zero-Intelligence: Biological Evolution, 3-9 Fig Wasps Play a Nash Equilibrium
4-1 Group Rationality and The Rationality of Individuals, 4-2 Why is Group Rationality Different From Rationality of Individuals?, 4-3 Group Rationality vs. Rationality of Individuals in Biological Evolution, 4-4 Group Rationality vs. Rationality of Individuals in Social Thought, 4-5 How to Enforce Socially Desirable Outcomes, 4-6 Cooperation of gas Stations in Long-Term Relationship Part I: Need For Cooperation, 4-7 Cooperation of Gas Stations in Long-Term Relationship Part II: Mechanism of Cooperation, 4-8 Reputation and Brand Name, 4-9 Cooperation in Loosely Knit Organization, 4-10 Summary of the Course, Final Message from the Instructor
Course: Introduction to Privacy - Part 2
Modules:
Data Inventory, Risk Assessment, Privacy by Design, Vendor Assessments, Other Assessments - Physical, Due Diligence and Attestation
Types of Documents, Management Documentation
Rights Transparency, Rights Subject Access Requests, Rights Erasure Rectification and Objection, Rights Automated Decision Making, Portability and Complaints
Training Approach, Training and Awareness Methods
Course: Arranging and Visualizing Data in R
Modules:
Data Science for Health Research: Specialization Introduction, Working with data in R, Course Roadmap, R and RStudio: What they are and how to get them Part 1, R and RStudio: What they are and how to get them Part 2, Objects and Assignments in R, Scripts, Installing R Packages, RStudio Projects
Using the Tidyverse to Read in your Data, Filter, Grouping and Summarizing, Grouping and Summarizing Guided Practice, Understanding R Functions (1), Data Pivoting, Data Pivoting Guided Practice, Creating New Variables with Mutate, Creating New Variables with Mutate Guided Practice, Selecting Variables and Arranging Rows, Selecting Variables and Arranging Rows Guided Practice, Joining Data, Joining Data Guided Practice, Understanding R Functions (2), Understanding R Functions (2) Guided Practice
Visualizing Data in ggplot2, The Grammar of Graphics, The Grammar of Graphics Guided Practice, A First Look at Geoms, A First Look at Geoms Guided Practice, Layering Geoms and Optional Aesthetics, Layering Geoms and Optional Aesthetics Guided Practice, Histograms, Histograms Guided Practice, Bar Plots, Bar Plots Guided Practice, Boxplots, Boxplots Guided Practice, Complex Plots with Tidy Code, Complex Plots with Tidy Code Guided Practice, Tidy Plots, Tidy Plots Guided Practice, Faceting, Faceting Guided Practice
Sharing your Work, Sharing your Work Guided Practice, R Markdown, R Markdown Guided Practice, Working with Data in R: Altogether
Course: Prototyping and Design
Modules:
Introduction to Prototyping and Design, Course Introduction
Prototyping:  What, Why, and How, Low-Fidelity Prototyping, Paper Prototype Example, Wireframing and Tool-Based Prototyping, Physical Low Fidelity Prototyping, Tool Example #1 (Pop), Tool Example #2 (Balsamiq), Limits of Prototyping, Paper Prototyping Assignment Video
Introduction to Design Principles & Patterns, Layout, Color and Consistency, Cultural Factors, Interaction Design Patterns, Direct Manipulation & User Control (Interview with Ben Shneiderman), Case Example:  Google's Material Design, Case Example: Apple Human Interface Guidelines, Design Critiques:  Eliciting and Giving Feedback
Introduction to Universal Design, Universal Design: Sensory Impairments, Universal Design: Cognitive Impairments, Universal Design: Physical Limitations, Universal Design: Tools and Standards, Design for Older Adults (Interview with Anne Marie Piper), Design for Children, Design and Socioeconomic Differences, Supporting Low-Literacy Self-Help (Interview with Apala Chavan)
Introduction to Platforms and Context, Mobile UI Design, Wearable, Automotive User Interfaces, IoT & Physical Computing