How to determine if someone is a subject matter expert in machine learning technology:

A subject matter expert typically possesses highly detailed knowledge and can quickly retrieve usable information to solve known problems. They are usually capable of creating, designing, evaluating, updating, maintaining, and solving new sets of problems. This framework can also be applied to other fields of expertise.

Level 2 High level knowledge and skills

High-level knowledge focuses primarily on the big picture and is easy to explain. Examples of machine learning techniques include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Level 3 Medium level knowledge and skills

Medium-level knowledge requires additional explanation and is not easy to convey. For example, machine learning involves analyzing variables — some of which are continuous, while others are discrete or categorical. Certain variables are referred to as target variables, while others are known as explanatory variables. Additionally, some variables may be unknown.

Level 4 Low level or detailed knowledge and skills

Low-level or detailed knowledge means you have acquired a deep understanding of terminology and have practiced extensive mathematical computations to validate results. This makes it challenging to explain concepts to those with medium- or high-level knowledge, as they may lack the necessary terminology and rarely engage in such computations. For example, machine learning involves mathematical processes such as regression, classification, clustering, association, control, and optimization.

Level 5 Very low level or very detailed knowledge and skills

Very low-level or highly detailed knowledge indicates that you have acquired an in-depth understanding of terminology and have practiced extensive mathematical computations to validate results. This level of expertise makes it significantly more difficult to explain concepts to those with medium or high-level knowledge, as they may lack the detailed terminology and rarely engage in such computations.

Examples of mathematical techniques used in machine learning include regression analysis, which encompasses ordinary least squares regression (OLSR), linear regression, logistic regression, stepwise regression, polynomial regression, and locally estimated scatterplot smoothing (LOESS).

Regression is not useful for finding equations with billions of parameters. Therefore, machine learning engineers, AI research scientists, and data scientists explore alternative solutions — using machines to discover patterns for classification, clustering, prediction, search, or retrieval of stored knowledge.

To reduce the need for manual feature engineering, these experts developed automated feature extraction techniques used in deep learning, such as convolutional neural networks (CNNs) for image data.

Image Processing with CNNs

In image processing, convolution refers to sliding a small filter (kernel) over an image to record features like edges, textures, or curves. This scanning process continues until the entire image is processed, forming a convolutional layer.

The transformed layers are passed to a pooling layer, which identifies key markers for classification. These summaries are then sent to an activation function layer, which applies criteria to highlight desired features and suppress irrelevant ones.

Finally, the output is passed to a fully connected layer that compares features against a database of labeled images. If classification is incorrect, experts adjust the activation weights — internal settings that determine how an image is identified.

Learning Algorithms

In neural networks, the learning algorithm supervises weight adjustments. Experts design the architecture and choose algorithms such as:

• Backpropagation: Identifies classification errors by moving backward through the network.
• Gradient Descent: Fine-tunes weights to minimize errors.
• Optimizers: Algorithms like Adam, RMSprop, and SGD accelerate training and stabilize weights.

Due to the scale of parameters, massive compute clusters are required.

Who Adjusts the Activation Weights?

The actual adjustments are done by algorithms. Experts control the process by designing the architecture, selecting training data, and configuring hyperparameters such as learning rate, batch size, and optimizer type.

Categories of Machine Learning Algorithms

Distance-Based Analysis

• K-Nearest Neighbor (KNN)
• Learning Vector Quantization (LVQ)
• Self-Organizing Map (SOM)

Decision Tree Algorithms

• Classification and Regression Tree (CART)
• Interactive Dichotomiser 3 (ID3)
• Chi-squared Automatic Interaction Detection (CHAID)
• Random Forest
• Conditional Decision Trees

Bayesian Algorithms

• Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Bayesian Belief Network (BBN)
• Bayesian Network

Clustering Algorithms

• K-Means
• K-Medians
• Partitioning Around Medoids (PAM)
• Hierarchical Clustering

Association Rule Mining

• Apriori Algorithm
• Eclat Algorithm
• FP-Growth Algorithm
• Context-Based Rule Mining

Artificial Neural Networks

• Perceptron
• Backpropagation
• Hopfield Network
• Radial Basis Function Network (RBFN)

Deep Learning Algorithms

• Deep Boltzmann Machine (DBM)
• Deep Belief Network (DBN)
• Convolutional Neural Network (CNN)
• Stacked Autoencoders

Dimensionality Reduction Algorithms

• Principal Component Analysis (PCA)
• Principal Component Regression (PCR)
• Partial Least Squares Regression (PLSR)
• Multidimensional Scaling (MDS)
• Linear Discriminant Analysis (LDA)
• Mixture Discriminant Analysis (MDA)
• Quadratic Discriminant Analysis (QDA)

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