Which of the following are use-cases of supervised learning?

Methods of supervised statistical learning

- Classification Problems
- Regression Problems

Types of supervised learning problems

Use cases of supervised statistical learning

Which ones are true about Supervised statistical learning?

Which of the following are examples of supervised machine learning? Select all that apply.

Categories of supervised learning algorithms

A Basic Supervised Statistical Learning Workflow

- Training set: to train the model
- Validation set (development set): to tune the model parameters
- Test set: to test the performance of the model on unobserved data

Division of dataset in supervised statistical learning

Feature scaling greatly affects which of the following supervised machine learning methods? 

1. Supervised learning allows you to collect data or produce a data output from the previous experience
2. Helps you to optimize performance criteria using experience
3 Supervised machine learning helps you to solve various types of real-world computation problems.

Adavantages of supervised learning

1. Decision boundary might be overtrained if your training set which doesn't have examples that you want to have in a class
2. You need to select lots of good examples from each class while you are training the classifier.
3. Classifying big data can be a real challenge.
4.Training for supervised learning needs a lot of computation time.


Disadvantages of Supervised Learning

1. Before doing anything else, you need to decide what kind of data is to be used as a training set
2. You need to decide the structure of the learned function and learning algorithm.
3. Gathere corresponding outputs either from human experts or from measurements

Best practices for Supervised Learning


The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition Paperback – February 28, 2020
https://www.amazon.com/Supervised-Learning-Workshop-Interactive-Understanding/dp/1800209045/ref=sr_1_1_sspa?dchild=1&keywords=Supervised+Learning&qid=1613836679&sr=8-1-spons&psc=1&spLa=ZW5jcnlwdGVkUXVhbGlmaWVyPUExQzMzWkdMTDlBVldRJmVuY3J5cHRlZElkPUEwMTY1NTYxMUNKMk8zN1YwR0ZZTyZlbmNyeXB0ZWRBZElkPUEwMjIwMDEwMjRUUzk1MllXUU5DSSZ3aWRnZXROYW1lPXNwX2F0ZiZhY3Rpb249Y2xpY2tSZWRpcmVjdCZkb05vdExvZ0NsaWNrPXRydWU=

The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms

Sequence models are statistical supervised learning models that predict/classify based on sequence data.

Similar to other supervised statistical models, sequence models are in two types:
- Sequence prediction
- Sequence classification

Sequence Models

The purpose of a statistical learning model is to use a statistical model and the training data to find the function $\hat{f}$ as an estimate of the unknown function $f$ that maps the matrix of input variables $X$ to the outcome vector $Y$, as best as possible.
$$Y = f(X) + \epsilon \approx \hat{f}(X)$$ 

Purpose of supervised statistical learning

In supervised learning, input values represent the set of known features or attributes provided to a model to make predictions. A critical first step in data preprocessing is partitioning the dataset to separate these input columns from the designated target values that the model aims to estimate.

Input Values

In information retrieval, search ranking is a task that involves imposing a rank on a set of items. Instead of simply determining if an item is relevant to a user's query, the objective is to assign a score to each relevant candidate and display the top-rated elements most prominently, utilizing machine learning and behavioral models to obtain query-dependent relevance scores.

Search Ranking

Sequence learning encompasses machine learning problems that require a model to ingest sequences of inputs, emit sequences of outputs, or both. Unlike standard tasks that process a fixed number of inputs independently, sequence learning models maintain context across successive observations, making them essential for handling varying-length data such as video snippets, text, or patient histories over time.

Sequence Learning

In supervised learning, a target value (often called a label) represents the designated output or objective variable that a model is trained to predict. During data preprocessing, the dataset is typically partitioned to isolate the target column from the input values, enabling the model to learn the mapping from features to the desired prediction.

Target Values

In standard supervised learning, we commonly rely on the independent and identically distributed (IID) assumption. This principle dictates that both the training and test data are drawn independently from the exact same underlying probability distribution, denoted as $$P(X,Y)$$. Without this strong assumption that $$P(\cdot) = Q(\cdot)$$ (where $$Q$$ is the test distribution), it would be impossible to justify using patterns learned from the training data to make predictions on new test data.

Independent and Identically Distributed (IID) Assumption

Supervised learning is a type of machine learning problem where the objective is to predict a designated unknown label based on known inputs. This approach requires a training dataset consisting of examples for which the ground truth labels are already known, allowing the model to learn the mapping from inputs to classifications.

University of Michigan - Ann Arbor

Claude

- Supervised statistical learning
- Unsupervised statistical learning
- Time series learning
- Semi-Supervised learning

Types of statistical learning

There are several categories for ML algorithms, including:
 - Supervised Learning
 - Semi-supervised Learning
 - Unsupervised Learning
 - Reinforcement Learning
 - Feature Learning (Representation Learning)

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.


Machine Learning Categories

A dataset is collections of data, which can be in the form of a table, network, text, image, video, sound, ...

Dataset

Machine learning is a field that studies how computer systems can utilize experience—typically in the form of data—to improve their performance on specific tasks. It integrates principles from statistics, data mining, and optimization, and is frequently employed as a primary method for implementing artificial intelligence solutions.

Machine Learning

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

Dive into Deep Learning

Unsupervised machine learning methods find structure in unlabeled data.
They are used when we DON'T have prior knowledge about the categories.

Unsupervised statistical learning

Semi-supervised learning lies somewhere between supervised and unsupervised learning. It takes small amount of labeled data to start learning and then moves on to huge quantity of unlabaled data to develop further.

Semi Supervised learning

Differences about the Supervised vs Unsupervised Machine learning

Training a model using labeled data and using this model to predict the labels for new data is known as __________.

Modeling the features of an unlabeled dataset to find hidden structure is known as _____.

A time series is a set of measurements of a quantitative variable taken at sequential intervals over a continuous period of time. This approach allows researchers to track trends and ongoing changes within a specific domain, such as recording a factory's weekly worker productivity over an entire year.

Time Series

Supervised Learning

In reinforcement learning, the machine goes through trial and error processes where it is rewarded/penalized for the actions it performs. The machine's goal is to maximize the total reward by leveraging previous attempts to make the next decision. The model starts from completely random trials and eventually leads to something with very sophisticated tactics and skills. Compared to supervised/unsupervised learning, reinforcement learning is much more advanced. A reinforcement model will continuously learn, unlike supervised/unsupervised models which all have an endpoint after the training and test data phases.

Reinforcement Learning

- Feature learning, also called representation learning use machine learning to discover not only the mapping from representation to output but also the representation itself. A feature learning algorithm can discover a good set of features for a simple task in minutes, or for a complex task in hours to months. Feature learning can be either supervised or unsupervised.

- Representation of information to be processed has a great importance in day to day life since it eases the task of information processing. For example usage of Arabic numerals instead of Roman numerals for mathematical operations.

- In the context of machine learning an acceptable representation of data makes the future learning tasks easy. The successive learning tasks decide the choice of information representation.



Feature Learning (Representation Learning)

https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine-ebook/dp/B012271YB2

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

•	Inductive reasoning 
•	Connectionism
•	Evolutionary Computation 
•	Bayes’ theorem 
•	Analogical modeling 

Key takeaway: Pedro Domingos believes the “Master algorithm” will be a mixture of different schools of machine learning, as each school of thought has strengths and weaknesses that challenge their ability to be applied universally. 


Machine learning schools of thought (as explained in ”The Master Algorithm” by Pedro Domingos):

What are the categories of machine learning algorithms?

- Number of data points (instances)
- Attributes (variables)
- Types of attributes (variables)
- Number of attributes (variables)
- Statistical measures

Characteristics of a dataset

Here are a list of free resources to find datasets. Most will be available in CSV format for easy integration into R or Python. 

Sample Datasets

It important to examine your dataset before starting to work with it for a machine learning task to:
- Understand how much missing data there is in the dataset.
- Get an idea of whether the features need further cleaning.
- Understand what techniques to use for analyzing the data.

The first step to analyze a dataset:

- 1) Equilibrium
- 2) Cyclic (periodic)
- 3) Random 
- 4) Complex

Wolfram's four classes of empirical data

Data can be distributed in several ways. Some of these include:
- Normal (Gaussian) distributions
- Uniform distributions
- Exponential distributions

Data Distributions

A machine learning model is the computational machinery designed to ingest data of a specific type, apply mathematical transformations, and output predictions. In modern applications, the focus is on statistical models whose internal transformations can be estimated and refined using a dataset.

Machine Learning Model

Before data can be fed into any machine learning model, its overall quality must be evaluated and improved. Real-world datasets are often plagued by flaws such as extreme outliers, faulty sensor measurements, and human recording errors. Addressing these discrepancies is an essential part of the data preparation process to ensure the model learns from reliable information.

Data Quality

In real-world machine learning scenarios, datasets are often not provided in a single, simple CSV file. Instead, they may be stored in a relational database, where information is distributed across multiple tables. For example, an e-commerce application might store customer addresses in one table and their purchase data in another, requiring practitioners to extract and merge these separate files before modeling.

Relational Database

Beyond standard categorical and numeric variables, deep learning practitioners frequently work with myriad unstructured data formats. These complex data types include text strings for natural language processing, images and point clouds for computer vision, and audio data. Handling these diverse formats requires specialized approaches and advanced tools compared to traditional tabular data.

Deep Learning Data Types

A data processing bottleneck occurs when the time taken to load, transform, and prepare complex data—such as high-resolution images or large text corpora—exceeds the time required for actual model training. To prevent data preprocessing from severely slowing down the overall machine learning pipeline, practitioners must implement efficient algorithms and utilize advanced tools.

Data Processing Bottleneck

The success of a machine learning model heavily depends on having the right data, as encapsulated by the principle 'garbage in, garbage out.' If a dataset contains mistakes, features that do not predict the target, or reflects societal prejudices by under-representing certain groups, the learning process will fail or produce biased, harmful results.

Machine Learning Dataset Quality

A machine learning example, interchangeably referred to as a data point, sample, or instance, represents an individual record within a dataset. To be useful for learning algorithms, each example is characterized by a specific numerical representation, forming the basis for a model's predictions.

Machine Learning Example

Comma-separated values (CSV) files are a ubiquitous format for storing tabular, spreadsheet-like data. In a CSV file, each line represents a distinct data record, and the individual fields within that record are separated by commas.

CSV File

A data batch, commonly referred to as a minibatch, is a discrete, smaller subset of a larger dataset that is processed simultaneously during a single iteration of machine learning model training or evaluation. Training typically requires multiple passes over a dataset, grabbing one minibatch at a time. Each minibatch consists of a tuple of features and corresponding labels. Processing data in minibatches rather than the entire dataset balances computational efficiency with hardware memory constraints.

Data Batch

When loading datasets for machine learning, the order in which data is read depends on the current mode. For training, data is typically read in a random order (shuffled) to properly update the model. For validation, data is read in a pre-defined, sequential order, which is important for debugging purposes and consistent evaluation.

Training vs. Validation Data Reading Order

Representational learning is a class of machine learning that focuses on automatically discovering the most appropriate way to represent data. By learning these representations directly from the data, it eliminates the need for manual feature engineering and allows models to effectively process and understand complex inputs.

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