Which one shows a basic machine learning workflow?

In this step, we choose:
- A feature representation of the dataset, e.g., image pixels
- Type of classifier to use, e.g., K-nearest neighbor classifier

Classification Problem Representation

In this step, we decide on the criterion to distinguish good vs. bad classifiers. For example, the percentage of correct predictions on test set.
The learned models will be evaluated on evaluation functions (also called objective functions or scoring functions) in order to distinguish between successful and unsuccessful models. The desired, external evaluation function might be different from the one the algorithm uses.

Classification Problem Evaluation

In this step, we search for the most optimal values for (hyper)parameters that give the best classification model for the chosen evaluation criterion. For example, we can try a range of values for the "k" parameter in k-nearest neighbor classifier.

Classification Problem Optimization

For a supervised learning algorithm to do well, you need to check the following four assumptions:
- **Low Bias**: Fits training set well on cost function (e.g., comparable to human level performance)
- **Low Variance**: Fits dev set on cost function
- Fits test set well on cost function
- Performs well in real world (e.g., happy users)

Chain of Assumptions in Supervised Statistical Learning

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. doi:10.1145/2347736.2347755

A few useful things to know about machine learning

•	Data leakage

•	Modeling non-linear data with linear models 

•	Forgetting to regularize data if needed

•	Substandard data cleaning

•	Failing to understand the underlying logic behind certain packages

Common errors in machine learning

University of Michigan - Ann Arbor

Supervised learning learns a function mapping inputs x to outputs y using labeled training examples (x,y). Linear regression, logistic regression, and neural networks are examples of supervised learning algorithms.

Supervised Learning

https://www.coursera.org/learn/python-machine-learning?utm_source=gg&utm_medium=sem&utm_content=01-CatalogDSA-ML1-US&campaignid=9918777773&adgroupid=100491712477&device=c&keyword=&matchtype=b&network=g&devicemodel=&adpostion=&creativeid=432357975999&hide_mobile_promo&gclid=CjwKCAjwnef6BRAgEiwAgv8mQb1CwbQkIUWRMGczmdtUo6LaKB5U4kg_Tet6Vk7sTpzIvIZtBwSxyxoC26sQAvD_BwE

Coursera: Applied Machine Learning in Python

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 functions designed to process, predict, or classify based on sequence data. A fundamental task in this domain is to estimate the joint probability of an entire sequence. When dealing with natural language data composed of discrete tokens, such as words, these estimated functions are commonly referred to as language models. Sequence models provide the capacity to evaluate the likelihood of sequences (for example, comparing the naturalness of outputs from translation systems), sample new sequences, and optimize for the most likely sequence outputs.

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

In traditional supervised learning applications, the output y was usually an integer or a real number.

Traditional Supervised Learning Outputs

Linear regression, logistic regression, and neural networks are examples of supervised learning algorithms.

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