# Uncertainty

### Probabilistic Graphical Models 2: Inference Coursera

These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are also a foundational tool in formulating many machine learning problems. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

### In Raw Numpy: t-SNE

To ensure the perplexity of each row of $$P$$, $$Perp(P_i)$$, is equal to our desired perplexity, we simply perform a binary search over each $$\sigma_i$$ until $$Perp(P_i)$$ our desired perplexity. It takes a matrix of negative euclidean distances and a target perplexity. Let's also define a p_joint function that takes our data matrix $$\textbf{X}$$ and returns the matrix of joint probabilities $$P$$, estimating the required $$\sigma_i$$'s and conditional probabilities matrix along the way: So we have our joint distributions $$p$$ and $$q$$. The only real difference is how we define the joint probability distribution matrix $$Q$$, which has entries $$q_{ij}$$.

### The 10 Algorithms Machine Learning Engineers Need to Know

Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. The textbook that we used is one of the AI classics: Peter Norvig's Artificial Intelligence -- A Modern Approach, in which we covered major topics including intelligent agents, problem-solving by searching, adversarial search, probability theory, multi-agent systems, social AI, philosophy/ethics/future of AI. Machine learning algorithms can be divided into 3 broad categories -- supervised learning, unsupervised learning, and reinforcement learning.Supervised learning is useful in cases where a property (label) is available for a certain dataset (training set), but is missing and needs to be predicted for other instances. You can think of linear regression as the task of fitting a straight line through a set of points.

After reading a bunch of job postings, I figured out that all it will take to become a real data scientist is five PhD's and 87 years of job experience. The field we call data science is still relatively young, yet already too broad for an individual to be an expert in every corner of it. We are all part generalist and part specialist. This distinction can be helpful when hiring data scientists too.

### Top Data Mining Algorithms Identified by IEEE & Related Python Resources

C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. Support vector machines(SVMs) are supervised learning models with learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of marked training examples, an SVM training algorithm builds a model that assigns new examples into one of marked categories. It is a link analysis algorithm and it assigns a numerical weighting called page rank to each element of a hyperlinked set of documents, with the purpose of "measuring" its relative importance within the set.

### Fitting Gaussian Process Models in Python

A common applied statistics task involves building regression models to characterize non-linear relationships between variables. When we write a function that takes continuous values as inputs, we are essentially implying an infinite vector that only returns values (indexed by the inputs) when the function is called upon to do so. To make this notion of a "distribution over functions" more concrete, let's quickly demonstrate how we obtain realizations from a Gaussian process, which result in an evaluation of a function over a set of points. We are going generate realizations sequentially, point by point, using the lovely conditioning property of mutlivariate Gaussian distributions.

### The Truth About Bayesian Priors and Overfitting

Have you ever thought about how strong a prior is compared to observed data? It features a cyclic process with one event represented by the variable d. There is only 1 observation of that event so it means that maximum likelihood will always assign everything to this variable that cannot be explained by other data. In the plot below you will see the truth which is y and 3 lines corresponding to 3 independent samples from the fitted resulting posterior distribution. Before you start to argue with my reasoning take a look at the plots where we plot the last prior vs the posterior and the point estimate from our generating process.

### Data Science and the Imposter Syndrome

I am not a real data scientist. After reading a bunch of job postings, I figured out that all it will take to become a real data scientist is five PhD's and 87 years of job experience. The field we call data science is still relatively young, yet already too broad for an individual to be an expert in every corner of it. We are all part generalist and part specialist.

### Understanding Support Vector Machine algorithm from examples (along with code)

In this article, I shall guide you through the basics to advanced knowledge of a crucial machine learning algorithm, support vector machines. The creation of a support vector machine in R and Python follow similar approaches, let's take a look now at the following code: Tuning parameters value for machine learning algorithms effectively improves the model performance. I am going to discuss about some important parameters having higher impact on model performance, "kernel", "gamma" and "C". In this article, we looked at the machine learning algorithm, Support Vector Machine in detail.