https://www.coursera.org/lecture/state-estimation-localization-self-driving-cars/lesson-1-the-linear-kalman-filter-7DFmY https://d3c33hcgiwev3.cloudfront.net/gWbwrisXEem4egrIUlgmqg.processed/full/360p/index.webm?Expires=1579392000&Signature=gLd7RN8aqZhrNLNLl-huuNsIrkWnUp8gPUAMNqk6Xnkx0lmkMKE8XdXs5v7GGSMvq9ieVeR7MAi2bDz6pxUhgWspfMtnZZ2k2ZpKKzKdNoiFHW-zBVcnFTq~yPyC0ssd1gHzenk2SHqPBu1BhkHTqz7nhdXU08UQS-Z1w7qhwcw_&Key-Pair-Id=APKAJLTNE6QMUY6HBC5A *** *** Note: Older short-notes from this site are posted on Medium: https://medium.com/@SayedAhmedCanada *** . *** *** . *** . *** . *** Sayed Ahmed BSc. Eng. in Comp. Sc. & Eng. (BUET) MSc. in Comp. Sc. (U of Manitoba, Canada) MSc. in Data Science and Analytics (Ryerson University, Canada) Linkedin: https://ca.linkedin.com/in/sayedjustetc Blog: …
Category: Math and Statistics for Data Science, and Engineering
Math and Statistics for Data Science, and Engineering
Jan 14
Misc. Optimization. Machine Learning
“What is machine learning optimization? Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with defining some kind of loss function/cost function and ends with minimizing the it using one or the other optimization routine.Sep 5, 2018″ https://towardsdatascience.com/demystifying-optimizations-for-machine-learning-c6c6405d3eea Ordered vector space “Given a vector space V over the real …
Jan 14
Design optimization
Design optimization https://medium.com/generative-design/design-optimization-2ec2ba3b40f7 Learning from nature https://medium.com/generative-design/learning-from-nature-fe5b7290e3de *** . **** . *** Sayed Ahmed BSc. Eng. in Comp. Sc. & Eng. (BUET) MSc. in Comp. Sc. (U of Manitoba, Canada) MSc. in Data Science and Analytics (Ryerson University, Canada) Linkedin: https://ca.linkedin.com/in/sayedjustetc Blog: http://Bangla.SaLearningSchool.com, http://SitesTree.com Online and Offline Training: http://Training.SitesTree.com FB Group on Learning/Teaching: https://www.facebook.com/banglasalearningschool Our …
Jan 14
The World is for Polymaths: An Interview with Sajid Amit, Academic, Researcher, and Development Strategist (Part One)
" The World is for Polymaths: An Interview with Sajid Amit, Academic, Researcher, and Development Strategist (Part One) Future Startup Face to Face | The Interview November 4, 2019 0 “When you have multiple lenses with which to consider a problem, it is an incredible advantage. The world is for polymaths,” says Sajid Amit as …
Jan 07
Math/Stat/CS/DS Topics that you need to know (with Cognitive, Psychomotor, Affective domain skills) to become a true and great Data Scientist
"The core topics are cross-validation, shrinkage methods (ridge regression, the LASSO, etc.), neural networks, gradient boosting, separating hyperplanes, support vector machines, basis expansion and regularization (e.g., smoothing splines, wavelet smoothing, kernel smoothing), generalized additive models, bump hunting, multivariate adaptive regression splines (MARS), self-organizing maps, mixture model-based clustering, ensemble learning, and p>>n problems. For computing, the …
Dec 31
Part 4: Some Basic Math/Stat Concepts for the wanna be Data Scientists
Part 4: Some Basic Math/Stat Concepts for the wanna be Data Scientists Also for the Engineers in General Quadratic form “In multivariate statistics, if is a vector of random variables, and is an -dimensional symmetric matrix, then the scalar quantity is known as a quadratic form in . ” Ref: https://en.wikipedia.org/wiki/Quadratic_form_(statistics) Please also check matrix …
Dec 30
Part 3: Some Basic Math/Stat Concepts for the wanna be Data Scientists
Conditional Probability and PDF “The conditional probability of an event B is the probability that the event will occur given the knowledge that an event A has already occurred. This probability is written P(B|A), notation for the probability of B given A. “ “In the case where events A and B are independent (where event …
Dec 29
Part 2: Some basic Math/Statistics concepts that Data Scientists (the true ones) will usually know/use
Part 2: Some basic Math/Statistics concepts that Data Scientists (the true ones) will usually know/use (came across, studied, learned, used) Covariance and Correlation “Covariance is a measure of how two variables change together, but its magnitude is unbounded, so it is difficult to interpret. By dividing covariance by the product of the two standard deviations, …
Dec 28
Part 1: Some Math/Stat Background that (true) Data Scientists will know/use: from the internet
Chebyshev’s inequality “In probability theory, Chebyshev’s inequality (also called the Bienaymé–Chebyshev inequality) guarantees that, for a wide class of probability distributions, no more than a certain fraction of values can be more than a certain distance from the mean. Specifically, no more than 1/k2 of the distribution’s values can be more than k standard deviations …
Dec 10
Overview on optimization concepts: From the Internet
Optimization Concepts: Convex sets: "A convex set is a set of points such that, given any two points A, B in that set, the line AB joining them lies entirely within that set. Intuitively, this means that the set is connected (so that you can pass between any two points without leaving the set) and …
