Machine Learning with R

Phương pháp tốt nhất để học Machine Learning là thiết kế và hoàn thiện những dự án nhỏ

Học máy với R

Thuật toán:

  1. Linear Discriminant Analysis (LDA)
  2. Classification and Regression Trees (CART).
  3. k-Nearest Neighbors (kNN).
  4. Support Vector Machines (SVM) with a linear kernel.
  5. Random Forest (RF)

Thuật toán: Random Forest

Thuật toán:

  1. Logistic Regression
  2. Recursive partitioning for classification (Basic and Bayesian)
  3. Random Forest
  4. Conditional Inference Tree
  5. Bayesian Networks
  6. Unbiased Non-parametric methods- Model Based (Logistic)
  7. Support Vector Machine
  8. Neural Network
  9. Lasso Regression

Mô hình: Artificial Neural Network (ANN): Feed-forward neural network

Mô hình:

  1. Feed-forward neural network
  2. Deep Autoencoders


Bài giảng đại chúng: Mật mã học hiện đại – Adi Shamir

Bài giảng đại chúng: Mật mã học hiện đại – Adi Shamir


Adi Shamir – ‘S’ trong hệ mã hoá khoá công khai RSA – là người khởi đầu cho nhiều hướng nghiên cứu của mật mã hiện đại.


– Phần 1: Cryptography: State of the Science

Abstract: This month we are celebrating the 40-th anniversary of the paper “New Directions in Cryptography” which was published by Diffie and Hellman in November 1976. This paper was a turning point in the history of cryptography, and established it for the first time as an academic research area. In this talk I’ll survey the main developments of the last 40 years, describe the current state of the field, and try to make some predictions about new cryptographic and cryptanalytic developments.
A more technical talk about my latest research can be an expanded version of my Crypto 2016 talk given three months ago.

– Phần 2: Memory-Efficient Algorithms for Finding Needles in Haystacks

(Joint work with Itai Dinur, Orr Dunkelman and Nathan Keller.)

Abstract: One of the most common tasks in cryptography and cryptanalysis is to find some interesting event (a needle) in an exponentially large collection (haystack) of N=2^n possible events, or to demonstrate that no such event is likely to exist. In particular, we are interested in finding needles which are defined as events that happen with an unusually high probability of p ≫ 1/N in a haystack which is an almost uniform distribution on N possible events. When the search algorithm can only sample values from this distribution, the best known time/memory tradeoff for finding such an event requires O(1/Mp^2) time given O(M) memory.

In this talk I will describe much faster needle searching algorithms in the common cryptographic setting in which the distribution is defined by applying some deterministic function f to random inputs.

Such a distribution can be modeled by a random directed graph with N vertices in which almost all the vertices have O(1) predecessors while the vertex we are looking for has an unusually large number of O(pN) predecessors. When we are given only a constant amount of memory, we propose a new search methodology which we call NestedRho. As p increases, such random graphs undergo several subtle phase transitions, and thus the log-log dependence of the time complexity T on p becomes a piecewise linear curve which bends four times. Our new algorithm is faster than the O(1/p^2) time complexity of the best previous algorithm in the full range of 1/N < p < 1 , and in particular it improves the previous time complexity by a significant factor of \sqrt{N} for any p in the range N^(−0.75) < p < N^(−0.5).
When we are given more memory, we show how to combine the NestedRho technique with the parallel collision search technique in order to further reduce its time complexity. Finally, we show how to apply our new search technique to more complicated distributions with multiple peaks when we want to find all the peaks whose probabilities are higher than p.

Cryptography: State of the Science

Delivered by ACM A.M. Turing Laureate Adi Shamir (2002)


Mối liên hệ giữa thu nhập của các nhà quản lý và giá cả trên các thị trường tài chính – GS. Jaksa Cvitanic

Compensation of Managers and Price Formation

On Friday, April 29, 2016, Caltech celebrated the launch of Break Through: The Caltech Campaign—an ambitious fundraising initiative that will help secure the Institute’s future. The celebration began with a symposium for the entire campus and JPL community: faculty, students, staff, alumni, family, and friends. A faculty member from each of Caltech’s six academic divisions explored briefly a seminal question and its potential to change the world. Following the symposium, all were invited to an outdoor festival marking the launch of the campaign.

Economist Jaksa Cvitanic presented “Compensation of Managers and Price Formation.” The way portfolio managers are compensated influences their actions on the job and, in turn, the way prices are formed in financial markets. In the talk, Cvitanic, who is Caltech’s Richard N. Merkin Professor of Mathematical Finance, highlights research that combines economic theory, mathematical models, and experiments to study those interactions.


Bài giảng: Phân bổ rủi ro dựa trên phân vị – GS. Paul Embrechts

Paul Embrechts – Quantile-based risk sharing

Presentation at the LSE Risk and Stochastics Conference 2016 by Paul Embrechts, Department of Mathematics, ETH Zürich


Học thống kê/kinh tế lượng trên Youtube




Youtube channel Content Software Online Materials? Remarks
Mike Marin [Intro] Basic Stats in R R Yes, good materials University British Columbia
Michael Butler [Intro] to R and Stats, Modern R Yes Good intro to R + Exercises
EZLearn [Intro] Basic Stats in R R Exercises w/ solutions
Renegade Thinking: Courtney Brown [Intro] Undergraduate Stats R Yes Good Lectures
Barton Poulson [Intro] Classical Stats, Programming & Solved Exercises R, Python, SPSS Yes Gives intro to Python, R, SPSS and launching an OLP
Ed Boone [Intro] Basic R and SAS R & SAS Yes [Intro] Basics of R and Descriptives R Yes OLP
Bryan Craven [Intro] Basic Stats in R R No
Laura Suttle (Revolution) [Intro] R tour for Beginners R No
Phil Chan [Intro] Classical and Bio-stats R, SPSS, Eviews No
Gordon Anthony Davis [Intro] R Programming Tutorial R No Thorough intro for beginners
David Langer [Intro] Basics of R R No Excellent pedagogical Skills
MrClean1796 [Intro] Math, Physics and Statistics, lecture & R R No
Brian Caffo Advanced & Bio-Stats, Reproducible Research R Yes, CourseraandGitHub Professor of Bio-statistics, Johns Hopkins Univ.
James Scott Advanced Stats R Yes, and GitHub Several Course Materials on GitHub
Derek Kane Machine Learning R Yes Excellent Videos, Fourier Analysis, Time series Forecasting
DataCamp Programming, DataViz, R Markdown [free] R Yes, paid. 9$ for students
Maria Nattestad DataViz R Personal Website Plotting in R for Biologists
Christoph Scherber Mixed, GLM, GLS, Contrasts R Yes
Librarian Womack Time Series, DataViz, BigData R Yes, Course and .R Materials online
Jarad Niemi R Workflow, Bayesian, Statistical Inference R Yes
Justin Esarey Bayesian, Categorical and Longitudinal Data, Machine Learning R Yes, lots and lots Political Scientist
Jeromy Anglim Research Methods R Blog:Psych & Stats,GitHub + Rmeetups and Notes on Gelman, Carlin, Stern, and Rubin
Erin Buchanan Under- & post-graduate Stats, SEM R, G*Power, Excel Yes Excellent pedagogical strategies
Richard McElreath From Basic to Advanced Bayesian Stats R and Stan Yes, lots Book lectures
edureka Data Science R, Hadoop, Python Yes, online learning plattaform R Intro w/ Hadoop [free]
Learn R R programming, stats on webiste R, Python Yes, andOne R Tip A Day On website, lots of starter’s code
Data School Machine Learning, Data Manipulation (dplyr) Python, R Yes, dplyr
Econometrics Academy Statistics (via Econometrics) R, STATA, SPSS Yes OLP, Excellent Materials and Resources
Jalayer Academy Basic Stats + Machine Learning R, Excel No Also Lectures
Michael Levy Authoring from R, Markdown, Shiny R No
Melvin L. Machine Learning, R Programming, PCA, DataViz R, Python, Gephi No Interesting Intro for Spark
OpenIntroOrg Intro to Stats/R plus Inference, Linear Models, Bayesian R Yes,CourseraandOpenIntro Coursera Courses, Resources in SAS


Youtube channel Content Software Online Materials? Remarks
Jonathan Tuke Basic Stats Matlab No
Saiful Yusoff PLS, Intro to MaxQDA SmartPLS, MaxQDA Yes BYU
James Gaskin SEM, PLS, Cluster SPSS, AMOS, SmartPLS Yes BYU
Quantitative Specialists Basic Stats SPSS No Upbeat videos
RStatsInstitute Basic Stats SPSS No Instructor at Udemy
how2stats Basic Stats, lecture and software demonstrations SPSS Yes Complete Classical Stats
BrunelASK Basic Stats SPSS
The Doctoral Journey Basic Stats SPSS Yes
StatisticsLectures Basic Stats, lecture format SPSS Yes discontinued, but thorough basic stats
Andy Field Classical Stats, lecture and software demonstrations SPSS Yes, registration needed Used heavely in Social Sciences
Quinnipiac University:Biostatistics Classical Stats SPSS No
The RMUoHP Biostatistics Basic and Bio-Stats SPSS, Excel No
PUB708 Team Classical Statistics SPSS, MiniTab No
Professor Ami Gates Classical Stats SPSS, Excel, StatCrunch Yes
H. Michael Crowson Intro and Basic Stats in several Softare SPSS, STATA, AMOS, LISREAL Yes?
Math Guy Zero Classical Stats + SEM SPSS, Excel, PLS No Lots of materials
BayesianNetworks Bayesian Statistics, SEM, Causality BayesianLab Yes
Khan Academy Programming 101 Python Yes
Mike’s SAS Short intro to SAS, SPSS SAS, SPSS No
Christian A. Wandeler Basic Stats PSPP No


Youtube channel Content Software Online Materials? Remarks
Stomp On Step 1 [Intro] Bio-Stats, Basic Lectures Yes USMLE
Khan Academy [Intro] Basic Stats, lecture format Lectures Yes
Joseph Nystrom [Intro] Basic Stats Lectures Yes Active & unorthodox teaching
Statistics Learning Centre [Intro] Basic Stats Lectures Yes Register to access materials
Brandon Foltz [Intro] Basic Stats Lectures soon Excellent visuals
David Waldo [Intro] Probability Theory Lectures No
Andrew Jahn [Intro] Basic Stats Lectures No FSL, AFNI and SPM [Neuro-immaging]
Professor Leonard [Intro] Stats and Maths Lectures No Excellent pedagogical skills
ProfessorSerna [Intro] Basic Stats Lectures No
Victor Lavrenko Machine Learning, Probabilistic, Cluster, PCA, Mixture Models Lectures Yes, very complete Excellent Content, and lots of it
Jeremy Balka’s Statistics Graduate-level Classical Stats, Lecture Lectures Yes, very thorough Excellent altogether, p-value vid great!
Methods Manchester Uni Discussion on a wide variety of methods, SEM Lectures Yes Methods Fair
Steve Grambow Series on Inference Lectures Yes Great Lectures on Inference [DUKE]
Statistics Corner: Terry Shaneyfelt Statistical Inference Lectures Yes from a clinical perspective
Michel van Biezen Complete Course of Stats Lectures Yes, 1, 2, 3 Thorough and complete, plus Physics and Maths
Oxford Education Bayesian statistics: a comprehensive course Lectures Yes
Nando de Freitas Machine Learning Lectures Yes, alsohere and here
Alex Smola Machine Learning Lectures Yes, slides and code
Abu (Abulhair) Saparov Machine Learning Lectures Yes Taught by Tom Mitchell and Maria-Florina Balcan
Geoff Gordon Machine Learning, Optimization Lectures Yes
MIT OpenCourseWare Probability Theory,Stochastic Processes Lectures Yes, here,and here
Alexander Ihler Machine Learning Lectures Yes, along w/ many others classes
Royal Statistical Society Important Statistical issues Lectures Yes Interesting topics
Ben Lambert Graduate and Advanced Stats Lectures No Asymptotic Behaviour of Estimators, SEM, EFA
DeepLearning TV Machine (and Deep) Learning Lectures No Excellent pedagogical skills
Mathematical Monk Machine Learning, and Probability Theory Lectures No


Ứng dụng luyện nói tiếng Anh thông minh ELSA

Ứng dụng luyện nói tiếng Anh thông minh ELSA

“Sản phẩm đã chiến thắng tại cuộc thi SXSWedu”

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ELSA (English Language Speech Assistant) là ứng dụng di động thông minh giúp người học Việt Nam phát âm chính xác và giao tiếp lưu loát như người bản xứ. Với hơn 30 trò chơi và 3,000 từ và câu thông dụng, ELSA giúp người học thực tập và hoàn thiện phát âm tiếng Anh.

Các trò chơi vui và cuốn hút, giúp người học dành điểm mỗi khi phát âm tốt và lên cấp, trở nên tự tin hơn với tiếng Anh của mình, chuẩn bị cho các kỳ thi TOEFL, TOEIC, IELTS cũng như các tình huống giao tiếp hằng ngày như phỏng vấn, du lịch, tình yêu, ẩm thực,… Đặc biệt, người chơi có thể kiểm tra phát âm chuẩn và phát âm của mình với bất kỳ từ ngữ nào trong tiếng Anh với tính năng Ask ELSA Now.

ELSA được xây dựng bởi công nghệ đặc biệt nhận diện giọng nói và hiểu nhu cầu của người học Việt Nam. Các vấn đề về phát âm, giao tiếp được các chuyên gia về ngôn ngữ hàng đầu phân tích và gợi ý cách luyện tập hiệu quả nhất. Cách sửa của ELSA đặc biệt phù hợp với người Việt, tập trung vào những âm và cách nhấn giọng người Việt hay sai, đưa lại hiệu quả tiến bộ trong thời gian ngắn nhất.

Học phát âm tiếng Anh và luyện giao tiếp với ELSA là một trải nghiệm vui, độc đáo, nhiều khám phá mới lạ giúp bạn hoàn thiện và tự tin vào tiếng Anh của mình.

Sản phẩm có thể chạy trên: Android; iOS

Trang chủ:


Time Series Analysis with R: ARMA, ARIMA, ARFIMA, ARCH, GARCH, VAR Models

Phân tích chuỗi thời gian với R:  Mô hình ARMA, ARIMA, ARFIMA (FARIMA), ARCH, GARCH, VAR

  • OTexts: Forecasting: principles and practice

ARIMA models

Dynamic regression models

Vector autoregressions (VAR)

  • Analytics Vidhya

A Complete Tutorial on Time Series Modeling in R

  • Avril Coghlan: Using R for Time Series Analysis

ARIMA Models

  • Quintuitive

ARMA Models for Trading

  • Milind Paradkar (Quantinsti)

Forecasting Stock Returns using ARIMA model

  • Portfolio Probe

A practical introduction to garch modeling (+EGARCH)

  • I. Ozkan: ARCH-GARCH Example with R

ARCH-GARCH Example with BIST, Oil and TL/USD Series

  • Alexios Ghalanos 


The rmgarch models: Background and properties (Multivariate GARCH: DCC-GARCH, GO-GARCH, Copula-GARCH)

  • Bernhard Pfaff

VAR, SVAR and SVEC Models: Implementation Within R Package vars (+Cointegration)

  • QuantStart: Time Series Analysis
  1. Beginner’s Guide to Time Series Analysis
  2. Serial Correlation in Time Series Analysis
  3. White Noise and Random Walks in Time Series Analysis
  4. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 1
  5. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 2
  6. Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 3
  7. Autoregressive Integrated Moving Average ARIMA(p, d, q) Models for Time Series Analysis
  8. Generalised Autoregressive Conditional Heteroskedasticity GARCH(p, q) Models for Time Series Analysis
  9. ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R
  10. State Space Models and the Kalman Filter
  11. Cointegrated Time Series Analysis for Mean Reversion Trading with R (Cointegration)