Machine Learning Guide

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**Machine Learning Guide**

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode’s details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

Latest Episodes:

MLG 010 Languages & Frameworks Mar 07, 2017

Show notes

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/10

Topics:

Recommended Languages and Frameworks:
- Python and TensorFlow are top recommendations for machine learning.
- Python's versatile libraries (NumPy, Pandas, Scikit-Learn) enable it to cover all areas of data science including data mining, analytics, and machine learning.
Language Choices:
- C/C++: High performance, suitable for GPU optimization but not recommended unless already familiar.
- Math Languages (R, MATLAB, Octave, Julia): Optimized for mathematical operations, particularly R preferred for data analytics.
- JVM Languages (Java, Scala): Suited for scalable data pipelines (Hadoop, Spark).
Framework Details:
- TensorFlow: Comprehensive tool supporting a wide range of ML tasks; notably improves Python's performance.
- Theano: First in symbolic graph framework, but losing popularity compared to newer frameworks.
- Torch: Initially favored for image recognition, now supports a Python API.
- Keras: High-level API running on top of TensorFlow or Theano for easier neural network construction.
- Scikit-learn: Good for shallow learning algorithms.

Comparisons:

C++ vs Python in ML: C++ offers direct GPU access for performance, but Python streamlined performance with frameworks that auto-generate optimized C code.
R and Python in Data Analytics: Python's Pandas and NumPy rival R with a strong general-purpose application beyond analytics.

Considerations:

Python's Ecosystem Benefits: Single programming ecosystem spans full data science workflow, crucial for integrated projects.
Emerging Trends: Keep an eye on Julia for future considerations in math-heavy operations and industry adoption.

Additional Notes:

Hardware Recommendations:
- Utilize Nvidia GPUs for machine learning due to superior support and integration with CUDA and cuDNN.
Learning Resources:
- TensorFlow's documentation and tutorials are highly recommended for learning due to their thoroughness and regular updates.
- Suggested learning order: Learn Python fundamentals, then proceed to TensorFlow.

Links

Other languages like Node, Go, Rust: why not to use them.
Best Programming Language for Machine Learning
Data Science Job Report 2017
An Overview of Python Deep Learning Frameworks
Evaluation of Deep Learning Toolkits
Comparing Frameworks: Deeplearning4j, Torch, Theano, TensorFlow, Caffe, Paddle, MxNet, Keras & CNTK - grain of salt, it's super heavy DL4J propaganda (written by them)

MLG 009 Deep Learning Mar 04, 2017

Show notes

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/9

Key Concepts:

Deep Learning vs. Shallow Learning: Machine learning is broken down hierarchically into AI, ML, and subfields like supervised/unsupervised learning. Deep learning is a specialized area within supervised learning distinct from shallow learning algorithms like linear regression.
Neural Networks: Central to deep learning, artificial neural networks include models like multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Neural networks are composed of interconnected units or "neurons," which are mathematical representations inspired by biological neurons.

Unique Features of Neural Networks:

Feature Learning: Neural networks learn to combine input features optimally, enabling them to address complex non-linear problems where traditional algorithms fall short.
Hierarchical Representation: Data can be processed hierarchically through multiple layers, breaking down inputs into simpler components that can be reassembled to solve complex tasks.

Applications:

Medical Cost Estimation: Neural networks can handle non-linear complexities such as feature interactions, e.g., age, smoking, obesity, impacting medical costs.
Image Recognition: Neural networks leverage hierarchical data processing to discern patterns such as lines and edges, building up to recognizing complex structures like human faces.

Computational Considerations:

Cost of Deep Learning: Deep learning's computational requirements make it expensive and resource-intensive compared to shallow learning algorithms. It's cost-effective to use when necessary for complex tasks but not for simpler linear problems.

Architectures & Optimization:

Different Architectures for Different Tasks: Specialized neural networks like CNNs are suited for image tasks, RNNs for sequence data, and DQNs for planning.
Neuron Types: Neurons in neural networks are referred to as activation functions (e.g., logistic sigmoid, relu) and differ based on tasks and architecture needs.

MLG 008 Math for Machine Learning Feb 23, 2017

Show notes

Mathematics essential for machine learning includes linear algebra, statistics, and calculus, each serving distinct purposes: linear algebra handles data representation and computation, statistics underpins the algorithms and evaluation, and calculus enables the optimization process. It is recommended to learn the necessary math alongside or after starting with practical machine learning tasks, using targeted resources as needed. In machine learning, linear algebra enables efficient manipulation of data structures like matrices and tensors, statistics informs model formulation and error evaluation, and calculus is applied in training models through processes such as gradient descent for optimization.

Links

Notes and resources at ocdevel.com/mlg/8
Try a walking desk - stay healthy & sharp while you learn & code

Come back here after you've finished Ng's course; or learn these resources in tandem with ML (say 1 day a week).

Recommended Approach to Learning Math

Direct study of mathematics before beginning machine learning is not necessary; essential math concepts are introduced within most introductory courses.
A top-down approach, where one starts building machine learning models and learns the underlying math as needed, is effective for retaining and appreciating mathematical concepts.
Allocating a portion of learning time (such as one day per week or 20% of study time) to mathematics while pursuing machine learning is suggested for balanced progress.

Linear Algebra in Machine Learning

Linear algebra is fundamental for representing and manipulating data as matrices (spreadsheets of features and examples) and vectors (parameter lists like theta).
Every operation involving input features and learned parameters during model prediction and transformation leverages linear algebra, particularly matrix and vector multiplication.
The concept of tensors generalizes vectors (1D), matrices (2D), and higher-dimensional arrays; tensor operations are central to frameworks like TensorFlow.
Linear algebra enables operations that would otherwise require inefficient nested loops to be conducted quickly and efficiently via specialized computation (e.g., SIMD processing on CPUs/GPUs).

Statistics in Machine Learning

Machine learning algorithms and error measurement techniques are derived from statistics, making it the most complex math branch applied.
Hypothesis and loss functions, such as linear regression, logistic regression, and log-likelihood, originate from statistical formulas.
Statistics provides both the probability framework (modelling distributions of data, e.g., housing prices in a city) and inference mechanisms (predicting values for new data).
Statistics forms the set of "recipes" for model design and evaluation, dictating how data is analyzed and predictions are made.

Calculus and Optimization in Machine Learning

Calculus is used in the training or "learning" step through differentiation of loss functions, enabling parameter updates via techniques such as gradient descent.
The optimization process involves moving through the error space (visualized as valleys and peaks) to minimize prediction error, guided by derivative calculations indicating direction and magnitude of parameter updates.
The particular application of calculus in machine learning is called optimization, more specifically convex optimization, which focuses on finding minima in "cup-shaped" error graphs.
Calculus is generally conceptually accessible in this context, often relying on practical rules like the power rule or chain rule for finding derivatives of functions used in model training.

The Role of Mathematical Foundations Post-Practice

Greater depth in mathematics, including advanced topics and the theoretical underpinnings of statistical models and linear algebra, can be pursued after practical familiarity with machine learning tasks.
Revisiting math after hands-on machine learning experience leads to better contextual understanding and practical retention.

Resources for Learning Mathematics

MOOCs, such as Khan Academy, provide video lessons and exercises in calculus, statistics, and linear algebra suitable for foundational knowledge.
Textbooks recommended in academic and online communities cover each subject and are supplemented by concise primer PDFs focused on essentials relevant to machine learning.
Supplementary resources like The Great Courses offer audio-friendly lectures for deeper or alternative exposure to mathematical concepts, although they may require adaptation for audio-only consumption.
Audio courses are best used as supplementary material, with primary learning derived from video, textbooks, or interactive platforms.

Summary of Math Branches in Machine Learning Context

Linear algebra: manipulates matrices and tensors, enabling data structure operations and parameter computation throughout the model workflow.
Statistics: develops probability models and inference mechanisms, providing the basis for prediction functions and error assessments.
Calculus: applies differentiation for optimization of model parameters, facilitating the learning or training phase of machine learning via gradient descent.
Optimization: a direct application of calculus focused on minimizing error functions, generally incorporated alongside calculus learning.

MLG 007 Logistic Regression Feb 19, 2017

Show notes

The logistic regression algorithm is used for classification tasks in supervised machine learning, distinguishing items by class (such as "expensive" or "not expensive") rather than predicting continuous numerical values. Logistic regression applies a sigmoid or logistic function to a linear regression model to generate probabilities, which are then used to assign class labels through a process involving hypothesis prediction, error evaluation with a log likelihood function, and parameter optimization using gradient descent.

Links

Notes and resources at ocdevel.com/mlg/7
Try a walking desk - stay healthy & sharp while you learn & code

Classification versus Regression in Supervised Learning

Supervised learning consists of two main tasks: regression and classification.
Regression algorithms predict continuous values, while classification algorithms assign classes or categories to data points.

The Role and Nature of Logistic Regression

Logistic regression is a classification algorithm, despite its historically confusing name.
The algorithm determines the probability that an input belongs to a specific class, using outputs between zero and one.

How Logistic Regression Works

The process starts by passing inputs through a linear regression function, then applying a logistic (sigmoid) function to produce a probability.
For binary classification, results above 0.5 usually indicate a positive class (for example, "expensive"), and results below 0.5 indicate a negative class ("not expensive").
Multiclass problems assign probabilities to each class, selecting the class with the highest probability using the arg max function.

Example Application: Housing Spreadsheet

An example uses a spreadsheet of houses with features like square footage and number of bedrooms, labeling each as "expensive" (1) or "not expensive" (0).
Logistic regression uses the spreadsheet data to learn the pattern that separates expensive houses from less expensive ones.

Steps in Logistic Regression

The algorithm follows three steps: predict (infer a class), evaluate error (calculate how inaccurate the guesses were), and train (refine the underlying parameters).
Predictions are compared to actual data, and the difference (error) is calculated via a log likelihood function, which accounts for how confident the prediction was compared to the true value.
Model parameters (theta values) are updated using gradient descent, which iteratively reduces the error by adjusting these values based on the derivative of the error function.

The Mathematical Foundation

The hypothesis function is the sigmoid or logistic function, with the formula: 1 / (1 + e^(-theta^T x)), where theta represents the parameters and x the input features.
The error function (cost function) for logistic regression uses log likelihood, aggregating errors over all data points to guide model learning.

Practical Considerations

Logistic regression finds a "decision boundary" on the graph (S-curve) that best separates classes such as "expensive" versus "not expensive."
When the architecture requires a proper probability distribution (sum of probabilities equals one), a softmax function is applied to the outputs, but softmax is not covered in this episode.

Composability in Machine Learning

Machine learning architectures are highly compositional, with functions nested within other functions - logistic regression itself is a function of linear regression.
This composability underpins more complex systems like neural networks, where each "neuron" can be seen as a logistic regression unit powered by linear regression.

Building Toward Advanced Topics

Understanding logistic and linear regression forms the foundation for approaching advanced areas of machine learning such as deep learning and neural networks.
The concepts of prediction, error measurement, and iterative training recur in more sophisticated models.

Resource Recommendations

The episode recommends the Andrew Ng Coursera course for deeper study into these concepts and details, especially for further exploration of multivariate regression and error functions.

MLG 006 Certificates & Degrees Feb 17, 2017

Show notes

People interested in machine learning can choose between self-guided learning, online certification programs such as MOOCs, accredited university degrees, and doctoral research, with industry acceptance and personal goals influencing which path is most appropriate. Industry employers currently prioritize a strong project portfolio over non-accredited certificates, and while master's degrees carry more weight for job applications, PhD programs are primarily suited for research interests rather than industry roles.

Links

Notes and resources at ocdevel.com/mlg/6
Try a walking desk - stay healthy & sharp while you learn & code

Learner Types and Self-Guided Education

Individuals interested in machine learning may be hobbyists, aspiring professionals, or scientists wishing to contribute to research in artificial intelligence.
Hobbyists can rely on structured resources, including curated syllabi and recommended online materials, to guide their self-motivated studies.
The "Andrew Ng Coursera" course is frequently recommended as an initial step for self-learners, and advanced resources such as "Artificial Intelligence: A Modern Approach" and "Deep Learning" textbooks are valuable later.

MOOCs and Online Certificates

MOOCs (Massive Open Online Courses) are widely available from platforms such as Coursera, Udacity, edX, and Khan Academy, but only Coursera and Udacity are commonly recognized for machine learning and data science content.
Coursera is typically recommended for individual courses; its specializations are less prominent in professional discussions.
Udacity offers both free courses and paid "nano degrees" which include structured mentoring, peer interaction, and project-based learning.
Although Udacity certificates demonstrate completion and the development of practical projects, they lack widespread recognition or acceptance from employers.
Hiring managers and recruiters consistently emphasize the value of a substantial project portfolio over non-accredited certificates for job-seekers.

University Degrees and Industry Recognition

Master's degrees in machine learning or computer science remain the most respected credentials for job applications in the industry, with requirements often officially listed in job postings.
The Georgia Tech OMSCS program provides an accredited, fully online Master's degree in Computer Science at a much lower cost than traditional programs, reportedly leveraging Udacity's course infrastructure.
In some cases, a strong portfolio can substitute for formal educational requirements, particularly if the applicant demonstrates practical and scalable machine learning project experience.
Portfolio strength is considered analogous to web development hiring, where demonstrated skills and personal projects can compensate for missing degree credentials.

PhD Pathways and Research Careers

A PhD is generally unnecessary for industry positions in machine learning; a master's degree or an exceptional portfolio is usually adequate.
Doctoral degrees are most useful for those seeking research roles or wishing to investigate complex theoretical questions in artificial intelligence, rather than working in standard industry applications.
PhD programs pay a stipend to students, though the compensation is much less than typical industry salaries, which should factor into an individual's decision-making process.

Considerations and Resources

Choosing an educational path depends on individual goals, available resources, and desired career trajectory; a portfolio of significant machine learning projects is universally beneficial regardless of the chosen route.
Community discussions and recruiter perspectives suggest that practical skills, proven through real-world projects, are highly valued in addition to or in place of formal degrees.
Interested individuals can review ongoing discussions and perspectives:

MLG 005 Linear Regression Feb 16, 2017

Show notes

Linear regression is introduced as the foundational supervised learning algorithm for predicting continuous numeric values, using cost estimation of Portland houses as an example. The episode explains the three-step process of machine learning - prediction via a hypothesis function, error calculation with a cost function (mean squared error), and parameter optimization through gradient descent - and details both the univariate linear regression model and its extension to multiple features.

Links

Notes and resources at ocdevel.com/mlg/5
Try a walking desk - stay healthy & sharp while you learn & code
Generate a podcast - use my voice to listen to any AI generated content you want

Linear Regression Overview of Machine Learning Structure

Machine learning is a branch of artificial intelligence, alongside statistics, operations research, and control theory.
Within machine learning, supervised learning involves training with labeled examples and is further divided into classification (predicting discrete classes) and regression (predicting continuous values).

Linear Regression and Problem Framing

Linear regression is the simplest and most commonly taught supervised learning algorithm for regression problems, where the goal is to predict a continuous number from input features.
The episode example focuses on predicting the cost of houses in Portland, using square footage and possibly other features as inputs.

The Three Steps of Machine Learning in Linear Regression

Machine learning in the context of linear regression follows a standard three-step loop: make a prediction, measure how far off the prediction is, and update the prediction method to reduce mistakes.
Predicting uses a hypothesis function (also called objective or estimate) that maps input features to a predicted value.

The Hypothesis Function

The hypothesis function is a formula that multiplies input features by coefficients (weights) and sums them to make a prediction; in mathematical terms, for one feature, it is: h(x) = theta_1 * x_1 + theta_0
- Here, theta_1 is the weight for the feature (e.g., square footage), and theta_0 is the bias (an average baseline).
With only one feature, the model tries to fit a straight line to a scatterplot of the input feature versus the actual target value.

Bias and Multiple Features

The bias term acts as the starting value when all features are zero, representing an average baseline cost.
In practice, using only one feature limits accuracy; including more features (like number of bedrooms, bathrooms, location) results in multivariate linear regression: h(x) = theta_0 + theta_1 * x_1 + theta_2 * x_2 + ... for each feature x_n.

Visualization and Model Fitting

Visualizing the problem involves plotting data points in a scatterplot: feature values on the x-axis, actual prices on the y-axis.
The goal is to find the line (in the univariate case) that best fits the data, ideally passing through the "center" of the data cloud.

The Cost Function (Mean Squared Error)

The cost function, or mean squared error (MSE), measures model performance by averaging squared differences between predictions and actual labels across all training examples.
Squaring ensures positive and negative errors do not cancel each other, and dividing by twice the number of examples (2m) simplifies the calculus in the next step.

Parameter Learning via Gradient Descent

Gradient descent is an iterative algorithm that uses calculus (specifically derivatives) to find the best values for the coefficients (thetas) by minimizing the cost function.
The cost function's surface can be imagined as a bowl in three dimensions, where each point represents a set of parameter values and the height represents the error.
The algorithm computes the slope at the current set of parameters and takes a proportional step (controlled by the learning rate alpha) toward the direction of the steepest decrease.
This process is repeated until reaching the lowest point in the bowl, where error is minimized and the model best fits the data.
Training will not produce a perfect zero error in practice, but it will yield the lowest achievable average error for the data given.

Extension to Multiple Variables

Multivariate linear regression extends all concepts above to datasets with multiple input features, with the same process for making predictions, measuring error, and performing gradient descent.
Technical details are essentially the same though visualization becomes complex as the number of features grows.

Essential Learning Resources

The episode strongly directs listeners to the Andrew Ng course on Coursera as the primary recommended starting point for studying machine learning and gaining practical experience with linear regression and related concepts.

MLG 004 Algorithms - Intuition Feb 12, 2017

Show notes

Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, unsupervised, and reinforcement learning, each with distinct methodologies for learning from data or experience.

Links

Notes and resources at ocdevel.com/mlg/4
Try a walking desk stay healthy & sharp while you learn & code

The Role of Machine Learning in Artificial Intelligence

Artificial intelligence includes subfields such as reasoning, knowledge representation, search, planning, and learning.
Learning connects to other AI subfields by enabling systems to improve from mistakes and past actions.

The Core Machine Learning Process

The machine learning process follows three steps: prediction (or inference), error evaluation (or loss calculation), and training (or learning).
In an example such as predicting chess moves, a move is made (prediction), the error or effectiveness of that move is measured (error function), and the underlying model is updated based on that error (learning).
This process generalizes to real-world applications like predicting house prices, where a model is trained on a large dataset with many features.

Data, Features, and Models

Datasets used for machine learning are typically structured as spreadsheets with rows as examples (e.g., individual houses) and columns as features (e.g., number of bedrooms, bathrooms, square footage).
Features are variables used by algorithms to make predictions and can be numerical (such as square footage) or categorical (such as "is downtown" yes/no).
The algorithm processes input data, learns the appropriate coefficients or weights for each feature through algebraic equations, and forms a model.
The combination of the algorithm (such as code in Python or TensorFlow) and the learned weights forms the model, which is then used to make future predictions.

Online Learning and Model Updates

After the initial training on a dataset, models can be updated incrementally with new data (called online learning).
When new outcomes are observed that differ from predictions, this new information is used to further train and improve the model.

Categories of Machine Learning Algorithms

Machine learning algorithms are broadly grouped into three categories: supervised, unsupervised, and reinforcement learning.
- Supervised learning uses labeled data, where the model is trained with known inputs and outputs, such as predicting prices (continuous values) or classes (like cat/dog/tree).
- Unsupervised learning finds similarities within data without labeled outcomes, often used for clustering or segmentation tasks such as organizing users for advertising.
- Reinforcement learning involves an agent taking actions in an environment to achieve a goal, receiving rewards or penalties, and learning the best strategies (policies) over time.

Examples and Mathematical Foundations

Regression algorithms like linear regression are commonly used supervised learning techniques to predict numeric outcomes.
The process is rooted in algebra and particularly linear algebra, where matrices represent datasets and the algorithm solves for optimal coefficient values.
The model's equation generated during training is used for making future predictions, and errors from predictions guide further learning.

Recommended Resources

MachineLearningMastery.com: Accessible articles on ML basics.
Podcast's own curated learning paths: ocdevel.com/mlg/resources.
The book "The Master Algorithm" offers an introductory and audio format overview of foundational machine learning algorithms and concepts.

MLG 003 Inspiration Feb 10, 2017

Show notes

AI is rapidly transforming both creative and knowledge-based professions, prompting debates on economic disruption, the future of work, the singularity, consciousness, and the potential risks associated with powerful autonomous systems. Philosophical discussions now focus on the socioeconomic impact of automation, the possibility of a technological singularity, the nature of machine consciousness, and the ethical considerations surrounding advanced artificial intelligence.

Links

Notes and resources at ocdevel.com/mlg/3
Try a walking desk stay healthy & sharp while you learn & code

Automation of the Economy

Artificial intelligence is increasingly capable of simulating intellectual tasks, leading to the replacement of not only repetitive and menial jobs but also high-skilled professions such as medical diagnostics, surgery, web design, and art creation.
Automation is affecting various industries including healthcare, transportation, and creative fields, where AI-powered tools are assisting or even outperforming humans in tasks like radiological analysis, autonomous vehicle operation, website design, and generating music or art.
Economic responses to these trends are varied, with some expressing fear about job loss and others optimistic about new opportunities and improved quality of life as history has shown adaptation following previous technological revolutions such as the agricultural, industrial, and information revolutions.
The concept of universal basic income (UBI) is being discussed as a potential solution to support populations affected by automation, as explored in several countries.
Public tools are available, such as the BBC's "Is your job safe?", which estimates the risk of job automation for various professions.

The Singularity

The singularity refers to a hypothesized point where technological progress, particularly in artificial intelligence, accelerates uncontrollably, resulting in rapid and irreversible changes to society.
The concept, popularized by thinkers like Ray Kurzweil, is based on the idea that after each major technological revolution, intervals between revolutions shorten, potentially culminating in an "intelligence explosion" as artificial general intelligence develops the ability to improve itself.
The possibility of seed AI, where machines iteratively create more capable versions of themselves, underpins concerns and excitement about a potential breakaway point in technological capability.

Consciousness and Artificial Intelligence

The question of whether machines can be conscious centers on whether artificial minds can experience subjective phenomena (qualia) analogous to human experience or whether intelligence and consciousness can be separated.
Traditional dualist perspectives, such as those of René Descartes, have largely been replaced by monist and functionalist philosophies, which argue that mind arises from physical processes and thus may be replicable in machines.
The Turing Test is highlighted as a practical means to assess machine intelligence indistinguishable from human behavior, raising ongoing debates in cognitive science and philosophy about the possibility and meaning of machine consciousness.

Risks and Ethical Considerations

Concerns about the ethical risks of advanced artificial intelligence include scenarios like Nick Bostrom's "paperclip maximizer," which illustrates the dangers of goal misalignment between AI objectives and human well-being.
Public figures have warned that poorly specified or uncontrolled AI systems could pursue goals in ways that are harmful or catastrophic, leading to debates about how to align advanced systems with human values and interests.

Further Reading and Resources

Books such as "The Singularity Is Near" by Ray Kurzweil, "How to Create a Mind" by Ray Kurzweil, "Consciousness Explained" by Daniel Dennett, and "Superintelligence" by Nick Bostrom offer deeper exploration into these topics.
Video lecture series like "Philosophy of Mind: Brain, Consciousness, and Thinking Machines" by The Great Courses provide overviews of consciousness studies and the intersection with artificial intelligence.

MLG 002 Difference Between Artificial Intelligence, Machine Learning, Data Science Feb 09, 2017

Show notes

Artificial intelligence is the automation of tasks that require human intelligence, encompassing fields like natural language processing, perception, planning, and robotics, with machine learning emerging as the primary method to recognize patterns in data and make predictions. Data science serves as the overarching discipline that includes artificial intelligence and machine learning, focusing broadly on extracting knowledge and actionable insights from data using scientific and computational methods.

Links

Notes and resources at ocdevel.com/mlg/2
Try a walking desk - stay healthy & sharp while you learn & code
Track privacy-first web traffic with OCDevel Analytics.

Data Science Overview

Data science encompasses any professional role that deals extensively with data, including but not limited to artificial intelligence and machine learning.
The data science pipeline includes data ingestion, storage, cleaning (feature engineering), and outputs in data analytics, business intelligence, or machine learning.
A data lake aggregates raw data from multiple sources, while a feature store holds cleaned and transformed data, prepared for analysis or model training.
Data analysts and business intelligence professionals work primarily with data warehouses to generate human-readable reports, while machine learning engineers use transformed data to build and deploy predictive models.
At smaller organizations, one person ("data scientist") may perform all data pipeline roles, whereas at large organizations, each phase may be specialized.
Wikipedia: Data Science describes data science as the interdisciplinary field for extracting knowledge and insights from structured and unstructured data.

Artificial Intelligence: Definition and Sub-disciplines

Artificial intelligence (AI) refers to the theory and development of computer systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. (Wikipedia: Artificial Intelligence)
The AI discipline is divided into subfields:
- Reasoning and problem solving
- Knowledge representation (such as using ontologies or knowledge graphs)
- Planning (selecting actions in an environment, e.g., chess- or Go-playing bots, self-driving cars)
- Learning
- Natural language processing (simulated language, machine translation, chatbots, speech recognition, question answering, summarization)
- Perception (AI perceives the world with sensors; e.g., cameras, microphones in self-driving cars)
- Motion and manipulation (robotics, transforming decisions into physical actions via actuators)
- Social intelligence (AI tuned to human emotions, sentiment analysis, emotion recognition)
- General intelligence (Artificial General Intelligence, or AGI: a system that generalizes across all domains at or beyond human skill)
Applications of AI include autonomous vehicles, medical diagnosis, creating art, proving theorems, playing strategy games, search engines, digital assistants, image recognition, spam filtering, judicial decision prediction, and targeted online advertising.
AI has both objective definitions (automation of intellectual tasks) and subjective debates around the threshold for "intelligence."
The Turing Test posits that if a human cannot distinguish an AI from another human through conversation, the AI can be considered intelligent.
Weak AI targets specific domains, while general AI aspires to domain-independent capability.
AlphaGo Movie depicts the use of AI planning and learning in the game of Go.

Machine Learning: Within AI

Machine learning (ML) is a subdiscipline of AI focused on building models that learn patterns from data and make predictions or decisions. (Wikipedia: Machine Learning)
Machine learning involves feeding data (such as spreadsheets of stock prices) into algorithms that detect patterns (learning phase) and generate models, which are then used to predict future outcomes.
Although ML started as a distinct subfield, in recent years it has subsumed many of the original AI subdisciplines, becoming the primary approach in areas like natural language processing, computer vision, reasoning, and planning.
Deep learning has driven this shift, employing techniques such as neural networks, convolutional networks (image processing), and transformers (language tasks), allowing generalizable solutions across multiple domains.
Reinforcement learning, a form of machine learning, enables AI systems to learn sequences of actions in complex environments, such as games or real-world robotics, by maximizing cumulative rewards.
Modern unified ML models, such as Google's Pathways and transformer architectures, can now tackle tasks in multiple subdomains (vision, language, decision-making) with a single framework.

Data Pipeline and Roles in Data Science

Data engineering covers obtaining and storing raw data from various data sources (datasets, databases, streams), aggregating into data lakes, and applying schema or permissions.
Feature engineering cleans and transforms raw data (imputation, feature transformation, selection) for machine learning or analytics.
Data warehouses store column-oriented, recent slices of data optimized for fast querying and are used by analysts and business intelligence professionals.
The analytics branch (data analysts, BI professionals) uses cleaned, curated data to generate human insights and reports.
- Data analysts apply technical and coding skills, while BI professionals often use specialized tools (e.g., Tableau, Power BI).
The machine learning branch uses feature data to train predictive models, automate decisions, and in some cases, trigger actions (robots, recommender systems).
The role of a "data scientist" can range from specialist to generalist, depending on team size and industry focus.

Historical Context of Artificial Intelligence

Early concepts of artificial intelligence appear in Greek mythology (automatons) and Jewish mythology (Golems).
Ramon Lull in the 13th century and Leonardo da Vinci constructed early automatons.
Contributions:
- Thomas Bayes (probability inference, 1700s)
- George Boole (logical reasoning, binary algebra)
- Gottlob Frege (propositional logic)
- Charles Babbage and Ada Byron/Lovelace (Analytical Engine, 1832)
- Alan Turing (Universal Turing Machine, 1936; foundational ideas on computing and AI)
- John von Neumann (Universal Computing Machine, 1946)
- Warren McCulloch, Walter Pitts, Frank Rosenblatt (artificial neurons, perceptron, foundation of connectionist/neural net models)
- John McCarthy, Marvin Minsky, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon (Dartmouth Workshop, 1956: "AI" coined)
- Newell and Simon (Heuristics, General Problem Solver)
- Feigenbaum (expert systems)
- GOFAI/symbolism (logic- and knowledge-based systems)
The "AI winter" followed the Lighthill report (1970s) due to overpromising and slow real-world progress.
AI resurgence in the 1990s was fueled by advances in computation, increased availability of data (the era of "big data"), and improvements in neural network methodologies (notably Geoffrey Hinton's optimization of backpropagation in 2006).
The 2010s saw dramatic progress, with companies such as DeepMind (acquired by Google in 2014) achieving state-of-the-art results in reinforcement learning and general AI research.
The Sub-disciplines of AI and other resources:

Further Learning Resources

MLG 001 Introduction Feb 01, 2017

Show notes

Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

MLG, Resources Guide
Gnothi (podcast project): website, Github

What is this podcast?

"Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
No math/programming experience required

Who is it for

Anyone curious about machine learning fundamentals
Aspiring machine learning developers

Why audio?

Supplementary content for commute/exercise/chores will help solidify your book/course-work

What it's not

News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
iTunesU issues

Planned episodes

What is AI/ML: definition, comparison, history
Inspiration: automation, singularity, consciousness
ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
Math overview: linear algebra, statistics, calculus
Linear models: supervised (regression, classification); unsupervised
Parts: regularization, performance evaluation, dimensionality reduction, etc
Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc

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