Re: Statistics For Machine Learning : Download Free Book
Agathe Thies
CSML leadership is pleased to announce the very popular certificate program in statistics and machine learning has been converted to a Princeton University minor. The change is effective for the Fall 2023 semester. More details to come.
All intelligent organisms have a nervous system, a way for communication to flow between the brain and the motor system and vice versa. Researchers at Princeton have taken a first step in developing this type of coordination for mechanical AI systems using the tools of machine learning.
A new project led by Brandon Stewart, associate professor of sociology and a researcher in the Office of Population Research, aims to learn what words, phrases and arguments successfully persuade people. The team will apply textual analysis tools and modern causal-inference designs to discover what features make a document persuasive. Using new large-language models, the team will create new machine-generated texts that possess these features, allowing the researchers to study systematically how specific attributes of the texts convince their audience.
Many methods from statistics and machine learning (ML) may, in principle, be used for both prediction and inference. However, statistical methods have a long-standing focus on inference, which is achieved through the creation and fitting of a project-specific probability model. The model allows us to compute a quantitative measure of confidence that a discovered relationship describes a 'true' effect that is unlikely to result from noise. Furthermore, if enough data are available, we can explicitly verify assumptions (e.g., equal variance) and refine the specified model, if needed.
By contrast, ML concentrates on prediction by using general-purpose learning algorithms to find patterns in often rich and unwieldy data1,2. ML methods are particularly helpful when one is dealing with 'wide data', where the number of input variables exceeds the number of subjects, in contrast to 'long data', where the number of subjects is greater than that of input variables. ML makes minimal assumptions about the data-generating systems; they can be effective even when the data are gathered without a carefully controlled experimental design and in the presence of complicated nonlinear interactions. However, despite convincing prediction results, the lack of an explicit model can make ML solutions difficult to directly relate to existing biological knowledge.
Classical statistics and ML vary in computational tractability as the number of variables per subject increases. Classical statistical modeling was designed for data with a few dozen input variables and sample sizes that would be considered small to moderate today. In this scenario, the model fills in the unobserved aspects of the system. However, as the numbers of input variables and possible associations among them increase, the model that captures these relationships becomes more complex. Consequently, statistical inferences become less precise and the boundary between statistical and ML approaches becomes hazier.
Statistics is a core component of data analytics and machine learning. It helps you analyze and visualize data to find unseen patterns. If you are interested in machine learning and want to grow your career in it, then learning statistics along with programming should be the first step. In this article, you will learn all the concepts in statistics for machine learning.
Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and visualizing empirical data. Descriptive statistics and inferential statistics are the two major areas of statistics. Descriptive statistics are for describing the properties of sample and population data (what has happened). Inferential statistics use those properties to test hypotheses, reach conclusions, and make predictions (what can you expect).
In statistics and probability, Gaussian (normal) distribution is a popular continuous probability distribution for any random variable. It is characterized by 2 parameters (mean μ and standard deviation σ). Many natural phenomena follow a normal distribution, such as the heights of people and IQ scores.
Statistics is a core component of machine learning. It helps you draw meaningful conclusions by analyzing raw data. In this article on Statistics for Machine Learning, you covered all the critical concepts that are widely used to make sense of data.
Machine learning (ML) is a subset of artificial intelligence (AI), which is defined as an ability of a machine to use historical data and algorithms to imitate how humans learn, gradually increasing its accuracy. Relying on machine learning services, companies across every industry deploy ML-based solutions to improve productivity, decision-making, product and service innovation, customer journey, and more.
The global machine learning market is steadily growing: in 2021, it was valued at $15.44 billion, and owing to the increasing adoption of technological advancements, it is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%. (Fortune Business Insights).
Although today's use cases for machine learning are becoming more varied, customer-centric applications remain the most common. According to Statista, 57% of respondents state customer experience represents the top ML and AI use cases.
Being an extension, not a replacement for human capabilities, machine learning enables companies to automate complex processes, improve the quality, effectiveness and creativity of employee decisions with rich analytics and pattern prediction capabilities, and uncover gaps and opportunities in the market to introduce new products and services, hyper personalize customer experience, and much more. (Accenture)
While AI and ML are becoming mainstream, the advances in AI and ML are being slowed by the shortage of employees with required skills. According to Statista, 82% of organizations need machine learning skills and only 12% of enterprises state the supply of ML skills is at an adequate level.
As seen from the statistics above, each company regardless of the industry has an endless number of ML adoption scenarios and high chances of success in case of following through on their initiative. If you want to advance your ML usage and achieve tangible economic gains, you need to take a holistic approach to AI and ML adoption. Rather than focusing on implementing scattered ML-powered solutions to address specific business needs, think of ML as an enabler of business transformation, enhanced decision-making, and modernized systems.
Statistics has been studied and used for more than a thousand years, with the first writings on the subject dating back to the 8th century. There are two main statistical methods: descriptive statistics and inferential statistics.
Descriptive statistics is the process of summarizing information about a sample using metrics like mean, median, mode, standard deviation, etc. This is sometimes used for exploratory data analysis leading to a larger project, or descriptive statistics may be the extent of the investigation.
Machine learning is a subset of artificial intelligence. It is the process of computers using large amounts of data to find patterns and make decisions without human intervention. Machine learning is used for tasks like text mining and sentiment analysis.
There are three methods of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning there is a target outcome variable. In unsupervised learning, there is no target and the machine is simply finding patterns and relationships within the data. The process of reinforcement learning involves an algorithm using trial and error to reach an objective.
While not centuries old, machine learning is not new and has been researched extensively since the 1950s. It has come into prominence in the past two decades due to the exponential growth in data collection and increased computing power.
Many machine learning techniques are drawn from statistics (e.g., linear regression and logistic regression), in addition to other disciplines like calculus, linear algebra, and computer science. But it is this association with underlying statistical techniques that causes many people to conflate the disciplines.
Interestingly, newer machine learning engineers and data scientists who use machine learning packages like scikit-learn in Python may be unaware of the underlying relationship between machine learning and statistics.
This abstraction of machine learning from statistics with the use of libraries is often why some individuals make the argument that knowledge of statistics is not necessary to do machine learning. While this may be true for more basic tasks, experienced data scientists and machine learning engineers draw on their knowledge probability and statistics to develop models.
Statistics and machine learning often get lumped together because they use similar means to reach a goal. However, the goals that they are trying to achieve are very different. The purpose of statistics is to make an inference about a population based on a sample. Machine learning is used to make repeatable predictions by finding patterns within data.
Statistics does not involve multiple subsets of data because you are not trying to make a prediction. The point of modeling in this situation is to display the relationship between data and the outcome variable. In addition, statistics relies on significance tests to determine the direction and magnitude of a relationship, discounting noise and confounding variables.
Because of the large number of variables in machine learning datasets, the models developed from them can be simultaneously extremely accurate and almost impossible to understand. Statistical models, on the other hand are typically easier to understand because they are based on fewer variables, and the accuracy of relationships is supported by tests of statistical significance.
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