Making Friends With Artificial Intelligence

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I took the Mathematical Biostatistics Boot Camp 1 last offering and I found it great. Now a new version of this course Mathematical Biostatistics Boot Camp 2 starts next week and it is an opportunity to continue interacting with great courses.

Information about the course is found below (retrieved from the course website).

About the Course

This class presents fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. Students having taken this class should be able to summarize samples, perform relevant hypothesis tests and perform a collection of two sample comparisons. Classical non-parametric methods and discrete data analysis methods are discussed.  The class is taught at a master’s of biostatistics introductory level and requires Mathematical Biostatistics Boot Camp 1 as a prerequisite.

Course Syllabus

  • Hypothesis Testing
  • Power and sample size and two group tests
  • Tests for binomial proportions
  • Two sample binomial tests, delta method
  • Fisher’s exact tests, Chi-squared tests
  • Simpson’s paradox, confounding
  • Retrospective case-control studies, exact inference for the odds ratio
  • Methods for matched pairs, McNemar’s, conditional versus marginal odds ratios
  • Non-parametric tests, permutation tests
  • Inference for Poisson counts
  • Multiplicity

Maximum Likelihood Estimator

When we apply Machine Learning, we often need to estimate the parameters of our model. It’s typically done by the use use of Maximum Likelihood Estimation (MLE). To understand the subject we found a very helpful material. To get this, access this link and enjoy that.

Nasa Machine Learning Challenge

This post is for you that like challenges. Harvard NASA Tournament Lab (NTL) and in partnership with the University of California San Diego and TopCoder published a contest.

Link to contest challenge video description.

Description

What happens when you mash-up Crowdsourcing, Open Innovation, human exploration, space exploration, and machine learning algorithms? You get this amazing, first of its kind, algorithmic challenge! Brought to you by the Harvard NASA Tournament Lab (NTL) and in partnership with the University of California San Diego and TopCoder … we’re excited to bring you the Collective Minds and Machine Learning Exploration Challenge.

The History of Internet Search and Google

I’m taking a course Understanding Media by Understanding Google and they point me out to a site that explain the Internet Search history. A found it very interesting. The link to post would be found here.

Masssive Online Course (MOOC) and Google

Look like that Google is trying to get a slice of MOOC. At least, is what is said by this article.

University of Waikato launches MOOC on data mining

The Waikato university launched the first New Zealand MOOC. The course has focus on Machine Learning and do an explanation on how to apply Weka for this. Here goes a summary about the course:

Everybody talks about Data Mining and Big Data nowadays. Weka is a powerful, yet easy to use tool for machine learning and data mining. This course introduces you to practical data mining.

You could access the course by https://weka.waikato.ac.nz/.

Schedule:

  • Class 1 – Getting started with Weka
  • Class 2 – Evaluation
  • Mid-course assessment
  • Class 3 – Simple classifiers
  • Class 4 – More classifiers
  • Class 5 – Putting it all together

Image Processing with MATLAB

Excellent blog about Image Processing with MATLAB. This blog describe topics related to image processing, both theoretical and practical, applying Matlab. It is a great space to learn standard approaches on image processing domain, discovery new approaches and apply methods in practice.

HRP258: Statistics in Medicine

New statistic course offered by Stanford, HRP258: Statistics in Medicine.

About the course

This course aims to provide a firm grounding in the foundations of probability and statistics. Specific topics include:

1. Describing data (types of data, data visualization, descriptive statistics)
2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)

Course syllabus

Week 1 – Descriptive statistics and looking at data
Week 2 – Review of study designs; measures of disease risk and association
Week 3 – Probability, Bayes’ Rule, Diagnostic Testing
Week 4 – Probability distributions
Week 5 – Statistical inference (confidence intervals and hypothesis testing)
Week 6 – P-value pitfalls; types I and type II error; statistical power; overview of statistical tests
Week 7 – Tests for comparing groups (unadjusted); introduction to survival analysis
Week 8 – Regression analysis; linear correlation and regression
Week 9 – Logistic regression and Cox regression

Machine Learning Video Library

Machine Learning Video Library

On Caltech page you can found a great library about different subject applied to Machine Learning. Take a lot and enjoy the library.