Data Science (DSC) 1
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Data Science Initiative 1
Data Science Initiative 1 3 credits in data and computational science, 1 credit in societal implications and opportunities, Data Science Initiative 1 elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration, and 1 credit capstone experience. Director We also offer an option as a 5-th Year Master's Program if you are an Sohini Ramachandran undergraduate at Brown. This allows you to substitute maximally 2 credits Direfctor of Graduate Studies with courses you have already taken. Samuel S. Watson Master of Science in Data Science Brown University's Data Science Initiative serves as a campus hub Semester I for research and education in data science. Engaging partners across DATA 1010 Probability, Statistics, and Machine 2 campus and beyond, the DSI 's mission is to facilitate and conduct both Learning domain-driven and fundamental research in data science, increase data DATA 1030 Hands-on Data Science 1 fluency and educate the next generation of data scientists, and ultimately DATA 1050 Data Engineering 1 explore the impact of the data revolution on culture, society, and social justice. We envision our role in the university and beyond as something to Semester II build over time, with the flexibility to meet the changing needs of Brown’s DATA 2020 Statistical Learning 1 students and research community. DATA 2040 Deep Learning and Special Topics in Data 1 The Master’s Program in Data Science (Master of Science, Science ScM) prepares students from a wide range of disciplinary backgrounds DATA 2080 Data and Society 1 for distinctive careers in data science. -
Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report)
EDUCAUSE CENTER FOR APPLIED RESEARCH Analytics in Higher Education Benefits, Barriers, Progress, and Recommendations EDUCAUSE CENTER FOR APPLIED RESEARCH Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations Contents Executive Summary 3 Key Findings and Recommendations 4 Introduction 5 Defining Analytics 6 Findings 8 Higher Education’s Progress to Date: Analytics Maturity 20 Conclusions 25 Recommendations 26 Methodology 27 Acknowledgments 29 Author Jacqueline Bichsel, EDUCAUSE Center for Applied Research Citation for this work: Bichsel, Jacqueline. Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report). Louisville, CO: EDUCAUSE Center for Applied Research, August 2012, available from http://www.educause.edu/ecar. Analytics in Higher Education Executive Summary Many colleges and universities have demonstrated that analytics can help significantly advance an institution in such strategic areas as resource allocation, student success, and finance. Higher education leaders hear about these transformations occurring at other institutions and wonder how their institutions can initiate or build upon their own analytics programs. Some question whether they have the resources, infrastruc- ture, processes, or data for analytics. Some wonder whether their institutions are on par with others in their analytics endeavors. It is within that context that this study set out to assess the current state of analytics in higher education, outline the challenges and barriers to analytics, and provide a basis for benchmarking progress in analytics. Higher education institutions, for the most part, are collecting more data than ever before. However, this study provides evidence that most of these data are used to satisfy credentialing or reporting requirements rather than to address strategic ques- tions, and much of the data collected are not used at all. -
Data Science (ML-DL-Ai)
Data science (ML-DL-ai) Statistics Multiple Regression Model Building and Evaluation Introduction to Statistics Model post fitting for Inference Types of Statistics Examining Residuals Analytics Methodology and Problem- Regression Assumptions Solving Framework Identifying Influential Observations Populations and samples Detecting Collinearity Parameter and Statistics Uses of variable: Dependent and Categorical Data Analysis Independent variable Describing categorical Data Types of Variable: Continuous and One-way frequency tables categorical variable Association Cross Tabulation Tables Descriptive Statistics Test of Association Probability Theory and Distributions Logistic Regression Model Building Picturing your Data Multiple Logistic Regression and Histogram Interpretation Normal Distribution Skewness, Kurtosis Model Building and scoring for Outlier detection Prediction Introduction to predictive modelling Inferential Statistics Building predictive model Scoring Predictive Model Hypothesis Testing Introduction to Machine Learning and Analytics Analysis of variance (ANOVA) Two sample t-Test Introduction to Machine Learning F-test What is Machine Learning? One-way ANOVA Fundamental of Machine Learning ANOVA hypothesis Key Concepts and an example of ML ANOVA Model Supervised Learning Two-way ANOVA Unsupervised Learning Regression Linear Regression with one variable Exploratory data analysis Model Representation Hypothesis testing for correlation Cost Function Outliers, Types of Relationship, -
Data Science: the Big Picture Data Science with R Exploratory Data Analysis with R Data Visualization with R (3-Part)
Deep Learning: The Future of AI @MatthewRenze #DevoxxUK Human Cat Dog Car Job Postings for Machine Learning Source: Indeed.com Average Salary by Job Type (USA) $108,000 $101,000 $100,000 Source: Stack Overflow 2017 What is deep learning? What can it do for me? How do I get started? What is deep learning? Deep Learning Deep Learning Artificial intelligence Machine learning Neural network Multiple hidden layers Hierarchical representations Makes predictions with data Deep Learning Artificial intelligence Machine learning Neural network Multiple hidden layers Hierarchical representations Makes predictions with data Artificial Machine Deep Intelligence Learning Learning Artificial Intelligence Explicit programming Explicit programming Encoding domain knowledge Explicit programming Encoding domain knowledge Statistical patterns detection Machine Learning Machine Learning Artificial Machine Statistics Intelligence Learning 푓 푥 푓 푥 Prediction Data Function 푓 푥 Prediction Data Function Cat Dog 푓 푥 Prediction Data Function Cat Dog Is cat? 푓 푥 Prediction Data Function Cat Dog Is cat? Yes Artificial Neuron 푥1 푥2 푦 푥3 inputs neuron outputs Artificial Neuron Σ Artificial Neuron Σ Artificial Neuron 휔1 휔2 휔3 Artificial Neuron 휔0 Artificial Neuron 휔0 휔1 휔2 휔3 Artificial Neuron 푥 1 휔0 휔1 푥2 휑 휔2 Σ 푦 푚 휔3 푥3 푦푘 = 휑 푤푘푗푥푗 푗=0 Artificial Neural Network Artificial Neural Network input hidden output Artificial Neural Network Forward propagation Artificial Neural Network Forward propagation Backward propagation Artificial Neural Network Artificial Neural Network -
Explainable Deep Learning Models in Medical Image Analysis
Journal of Imaging Review Explainable Deep Learning Models in Medical Image Analysis Amitojdeep Singh 1,2,* , Sourya Sengupta 1,2 and Vasudevan Lakshminarayanan 1,2 1 Theoretical and Experimental Epistemology Laboratory, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada; [email protected] (S.S.); [email protected] (V.L.) 2 Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada * Correspondence: [email protected] Received: 28 May 2020; Accepted: 17 June 2020; Published: 20 June 2020 Abstract: Deep learning methods have been very effective for a variety of medical diagnostic tasks and have even outperformed human experts on some of those. However, the black-box nature of the algorithms has restricted their clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clinical deployment, and the areas requiring further research are discussed here from a practical standpoint of a deep learning researcher designing a system for the clinical end-users. Keywords: explainability; explainable AI; XAI; deep learning; medical imaging; diagnosis 1. Introduction Computer-aided diagnostics (CAD) using artificial intelligence (AI) provides a promising way to make the diagnosis process more efficient and available to the masses. Deep learning is the leading artificial intelligence (AI) method for a wide range of tasks including medical imaging problems. -
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018)
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018) Chika Yinka-Banjo, PhD Ayorkor Korsah, PhD University of Lagos Ashesi University Nigeria Ghana Outline • Introduction to Machine learning • Reinforcement learning definitions • Example reinforcement learning problems • The Markov decision process • The optimal policy • Value function & Q-value function • Bellman Equation • Q-learning • Building a simple Q-learning agent (coding) • Recap • Where to go from here? Introduction to Machine learning • Artificial Intelligence (AI) is the study and design of Intelligent agents. • An Intelligent agent can perceive its environment through sensors and it can act on its environment through actuators. • E.g. Agent: Humanoid robot • Environment: Earth? • Sensors: Camera, tactile sensor etc. • Actuators: Motors, grippers etc. • Machine learning is a subfield of Artificial Intelligence Branches of AI Introduction to Machine learning • Machine learning techniques learn from data without being explicitly programmed to do so. • Machine learning models enable the agent to learn from its own experience by extracting useful information from feedback from its environment. • Three types of learning feedback: • Supervised learning • Unsupervised learning • Reinforcement learning Branches of Machine learning Supervised learning • Supervised learning: the machine learning model is trained on many labelled examples of input-output pairs. • Such that when presented with a novel input, the model can estimate accurately what the correct output should be. • Data(x, y): x is input data, y is label Supervised learning task in the form of classification • Goal: learn a function to map x -> y • Examples include; Classification, regression object detection, image captioning etc. Unsupervised learning • Unsupervised learning: here the model extract useful information from unlabeled and unstructured data. -
An Artificial Neural Networks Primer with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System ’
‘An Artificial Neural Networks Primer with Financial Applications Examples in Financial Distress Predictions and Foreign Exchange Hybrid Trading System ’ by Dr Clarence N W Tan, PhD Bachelor of Science in Electrical Engineering Computers (1986), University of Southern California, Los Angeles, California, USA Master of Science in Industrial and Systems Engineering (1989), University of Southern California Los Angeles, California, USA Masters of Business Administration (1989) University of Southern California Los Angeles, California, USA Graduate Diploma in Applied Finance and Investment (1996) Securities Institute of Australia Diploma in Technical Analysis (1996) Australian Technical Analysts Association Doctor of Philosophy Bond University (1997) URL: http://w3.to/ctan/ E-mail: [email protected] School of Information Technology, Bond University, Gold Coast, QLD 4229, Australia Table of Contents Table of Contents 1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND ARTIFICIAL NEURAL NETWORKS ..........................................................................................................................................2 1.1 INTRODUCTION ...........................................................................................................................2 1.2 ARTIFICIAL INTELLIGENCE ..........................................................................................................2 1.3 ARTIFICIAL INTELLIGENCE IN FINANCE .......................................................................................4 1.3.1 Expert System -
Using Data Analytics to Detect Fraud
Using Data Analytics to Detect Fraud Gerard M. Zack, CFE, CPA, CIA, CCEP President, Zack, P.C. Introduction to Data Analytics ®2014 Association of Certified Fraud Examiners, Inc. 1 of 16 ®2014 Association of Certified Fraud Examiners, Inc. Course Objectives . How data analytics can be used to detect fraud . Different tools to perform data analytics . How to walk through the full data analytics process . Red flags of fraud that appear in the data . Data analytics tests that can be used to detect fraud . How to analyze non-numeric data, such as text and timelines, for signs of fraud ®2014 Association of Certified Fraud Examiners, Inc. 2 of 16 Introduction to Data Analytics . Data analytics, as it applies to fraud examination, refers to the use of analytics software to identify trends, patterns, anomalies, and exceptions within data. ®2014 Association of Certified Fraud Examiners, Inc. 3 of 16 Introduction to Data Analytics . Especially useful when fraud is hidden in large data volumes and manual checks are insufficient . Can be used reactively or proactively ®2014 Association of Certified Fraud Examiners, Inc. 4 of 16 Introduction to Data Analytics . Effective data analysis requires: • Translating knowledge of organization and common fraud indicators into analytics tests • Effectively using technological tools • Resolving errors in data output due to incorrect logic or scripts • Applying fraud investigation skills to the data analysis results in order to detect potential instances of fraud ®2014 Association of Certified Fraud Examiners, Inc. 5 of 16 Introduction to Data Analytics . Data analysis techniques alone are unlikely to detect fraud; human judgment is needed to decipher results. -
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Lecture 23: Recurrent Neural Network [SCS4049-02] Machine Learning and Data Science
Lecture 23: Recurrent Neural Network [SCS4049-02] Machine Learning and Data Science Seongsik Park ([email protected]) AI Department, Dongguk University Tentative schedule week topic date (수 / 월) 1 Machine Learning Introduction & Basic Mathematics 09.02 / 09.07 2 Python Practice I & Regression 09.09 / 09.14 3 AI Department Seminar I & Clustering I 09.16 / 09.21 4 Clustering II & Classification I 09.23 / 09.28 5 Classification II (추석) / 10.05 6 Python Practice II & Support Vector Machine I 10.07 / 10.12 7 Support Vector Machine II & Decision Tree and Ensemble Learning 10.14 / 10.19 8 Mid-Term Practice & Mid-Term Exam 10.21 / 10.26 9 휴강 & Dimensional Reduction I 10.28 / 11.02 10 Dimensional Reduction II & Neural networks and Back Propagation I 11.04 / 11.09 11 Neural networks and Back Propagation II & III 11.11 / 11.16 12 AI Department Seminar II & Convolutional Neural Network 11.18 / 11.23 13 Python Practice III & Recurrent Neural network 11.25 / 11.30 14 Model Optimization & Autoencoders 12.02 / 12.07 15 Final exam (휴강)/ 12.14 1/22 Introduction of recurrent neural network (RNN) • Rumelhart, et. al., Learning Internal Representations by Error Propagation (1986) • fx(1); :::; x(t−1); x(t); x(t+1); :::; x(N)g와 같은 sequence를 처리하는데 특화 • RNN 은 긴 시퀀스를 처리할 수 있으며, 길이가 변동적인 시퀀스도 처리 가능 • Parameter sharing: 전체 sequence에서 파라미터를 공유함 • CNN은 시퀀스를 다룰 수 없나? • 시간 축으로 1-D convolution ) 시간 지연 신경망도 가능하지만 깊이가 얕음 (shallow) • RNN은 깊은 구조(deep)가 가능함 • Data type • 일반적으로 xt 는 시계열 데이터 (time series, temporal data) • 여기서 t = 1; 2; :::; N은 time step 또는 sequence 내에서의 순서 • 전체 길이 N은 변동적 2/22 Recurrent Neurons Up to now we have mostly looked at feedforward neural networks, where the activa‐ tions flow only in one direction, from the input layer to the output layer (except for a few networks in Appendix E). -
Google Analytics User Guide
Page | 1 What is Google Analytics? Google Analytics is a cloud-based analytics tool that measures and reports website traffic. It is the most widely used web analytics service on the Internet. Why should we all use it? Google Analytics helps you analyze visitor traffic and paint a complete picture of your audience and their needs. It gives actionable insights into how visitors find and use your site, and how to keep them coming back. In a nutshell, Google Analytics provides information about: • What kind of traffic does your website generate – number of sessions, users and new users • How your users interact with your website & how engaged they are – pages per session, average time spent on the website, bounce rate, how many people click on a specific link, watch a video, time spent on the webpage • What are the most and least interesting pages – landing and exit pages, most and least visited pages • Who visits your website – user`s geo location (i.e. city, state, country), the language they speak, the browser they are using, the screen resolution of their device • What users do once they are on your website – how long do users stay on the website, which page is causing users to leave most often, how many pages on average users view • When users visit your website – date & time of their visits, you can see how the user found you. • Whether visitors came to your website through a search engine (Google, Bing, Yahoo, etc.), social networks (Facebook, Twitter, etc.), a link from another website, or a direct type-in. -
Nine Common Types of Data Mining Techniques Used in Predictive Analytics
1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better understand the variables driving success. Predictive analytics relies on formulas that compare past examples of success and failure, and then use these formulas to predict future outcomes. Predictive analytics, pattern recognition and classification problems are not new. Long used in the financial services and insurance industries, predictive analytics is about using statistics, data mining and game theory to analyze current and historical facts in order to make predictions about future events. The value of predictive analytics is relatively obvious. The more you understand customer behavior and motivations the more effective your marketing. The more you know why some customers are loyal, how to attract and retain different customer segments, the more you can develop relevant compelling messages and offers. Predicting customer buying and product preferences and habits requires an analytical framework that enables you to discover meaningful patterns and relationships within customer data in order to do better message targeting, drive customer value and loyalty. Predictive models have been used in business to assess the risk or potential associated with a particular set of conditions as a way to guide decision making. Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. Marketing and sales professionals are beginning to capture and analyze many different types of customer data: attitudinal, behavioral, and 2 transactional related to purchasing and product preferences in order to make predictions about future buying behavior. -
Budgeted Reinforcement Learning in Continuous State Space
Budgeted Reinforcement Learning in Continuous State Space Nicolas Carrara∗ Edouard Leurent∗ SequeL team, INRIA Lille – Nord Europey SequeL team, INRIA Lille – Nord Europey [email protected] Renault Group, France [email protected] Romain Laroche Tanguy Urvoy Microsoft Research, Montreal, Canada Orange Labs, Lannion, France [email protected] [email protected] Odalric-Ambrym Maillard Olivier Pietquin SequeL team, INRIA Lille – Nord Europe Google Research - Brain Team [email protected] SequeL team, INRIA Lille – Nord Europey [email protected] Abstract A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an – adjustable – threshold. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving3. 1 Introduction Reinforcement Learning (RL) is a general framework for decision-making under uncertainty. It frames the learning objective as the optimal control of a Markov Decision Process (S; A; P; Rr; γ) S×A with measurable state space S, discrete actions A, unknown rewards Rr 2 R , and unknown dynamics P 2 M(S)S×A , where M(X ) denotes the probability measures over a set X .