Machine Learning Guide

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Full notes at ocdevel.com/mlg/mla-3

Comparison of different data storage options when working with your ML models.

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Full notes at ocdevel.com/mlg/mla-2

Some numerical data nitty-gritty in Python.

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Full notes at ocdevel.com/mlg/mla-1

Reboot on the MLG episode, with more confident recommends.

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Introduction to reinforcement learning concepts. ocdevel.com/mlg/29 for notes and resources.

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Hyperparameters part 2: hyper-search, regularization, SGD optimizers, scaling. ocdevel.com/mlg/28 for notes and resources

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Hyperparameters part 1: network architecture. ocdevel.com/mlg/27 for notes and resources

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Community project & intro to Bitcoin/crypto + trading. ocdevel.com/mlg/26 for notes and resources

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Convnets or CNNs. Filters, feature maps, window/stride/padding, max-pooling. ocdevel.com/mlg/25 for notes and resources

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TensorFlow, Pandas, Numpy, Scikit-Learn, Keras, TensorForce. ocdevel.com/mlg/24 for notes and resources

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RNN review, bi-directional RNNs, LSTM & GRU cells. ocdevel.com/mlg/23 for notes and resources

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Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources

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Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources

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Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources

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Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources

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At this point, browse #importance:essential on ocdevel.com/mlg/resources with the 45m/d ML, 15m/d Math breakdown.

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Can AI be conscious? ocdevel.com/mlg/16 for notes and resources

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Performance evaluation & improvement. ocdevel.com/mlg/15 for notes and resources

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Full notes at ocdevel.com/mlg/14

Speed run of Anomaly Detection, Recommenders(Content Filtering vs Collaborative Filtering), and Markov Chain Monte Carlo (MCMC). ocdevel.com/mlg/14 for notes and resources

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Speed run of Support Vector Machines (SVMs) and Naive Bayes Classifier. ocdevel.com/mlg/13 for notes and resources

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Speed-run of some shallow algorithms: K Nearest Neighbors (KNN); K-means; Apriori; PCA; Decision Trees

ocdevel.com/mlg/12 for notes and resources

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Languages & frameworks comparison. Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow. ocdevel.com/mlg/10 for notes and resources

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Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron. ocdevel.com/mlg/9 for notes and resources

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Introduction to the branches of mathematics used in machine learning. Linear algebra, statistics, calculus. ocdevel.com/mlg/8 for notes and resources

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Full notes at ocdevel.com/mlg/7. See Andrew Ng Week 3 Lecture Notes

Overview

• Logistic Function: A sigmoid function transforming linear regression output to logits, providing a probability between 0 and 1.
• Binary Classification: Logistic regression deals with binary outcomes, determining either 0 or 1 based on a threshold (e.g., 0.5).
• Error Function: Uses log likelihood to measure the accuracy of predictions in logistic regression.
• Gradient Descent: Optimizes the model by adjusting weights to minimize the error function.

Classification vs Regression

• Classification: Predicts a discrete label (e.g., a cat or dog).
• Regression: Predicts a continuous outcome (e.g., house price).

Practical Example

• Train on a dataset of house features to predict if a house is 'expensive' based on labeled data.
• Automatically categorize into 0 (not expensive) or 1 (expensive) through training and gradient descent.

Logistic Regression in Machine Learning

• Neurons in Neural Networks: Act as building blocks, as logistic regression is used to create neurons for more complex models like neural networks.
• Composable Functions: Demonstrates the compositional nature of machine learning algorithms where functions are built on other functions (e.g., logistic built on linear).

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Full notes at ocdevel.com/mlg/6

Pursuing Machine Learning:

• Individuals may engage with machine learning for self-education, as a hobby, or to enter the industry professionally.
• Use a combination of resources, including podcasts, online courses, and textbooks, for a comprehensive self-learning plan.

Online Courses (MOOCs):

• MOOCs, or Massive Open Online Courses, offer accessible education.
• Key platforms: Coursera and Udacity. Coursera is noted for standalone courses; Udacity offers structured nanodegrees.
• Udacity nanodegrees include video content, mentoring, projects, and peer interaction, priced at $200/month.

Industry Recognition:

• Udacity nanodegrees are currently not widely recognized or respected by employers.
• Emphasize building a robust portfolio of independent projects to augment qualifications in the field.

Advanced Degrees:

• Master’s Degrees:
• Valued by employers, provide an edge in job applications.
• Example: Georgia Tech's OMSCS (Online Master’s of Science in Computer Science) offers a cost-effective ($7,000) online master’s program.
• PhD Programs:
• Embark on a PhD for in-depth research in AI rather than industry entry. Program usually pays around $30,000/year.
• Compare industry roles (higher pay, practical applications) vs. academic research (lower pay, exploration of fundamental questions).

Career Path Decisions:

• Prioritize building a substantial portfolio of projects to bypass formal degree requirements and break into industry positions.
• Consider enriching your qualifications with a master's degree, or eventually pursue a PhD if deeply interested in pioneering AI research.

Discussion and Further Reading:

• See online discussions about degrees/certifications: 1 2 3 4

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Show notes at ocdevel.com/mlg/5. See Andrew Ng Week 2 Lecture Notes

Key Concepts

• Machine Learning Hierarchy: Explains the breakdown into supervised, unsupervised, and reinforcement learning with an emphasis on supervised learning, which includes classification and regression.
• Supervised Learning: Divided into classifiers and regressors, with this episode focusing on linear regression as an introduction to regressor algorithms.
• Linear Regression: A basic supervised algorithm used for estimating continuous numeric outputs, such as predicting housing prices.

Process of Linear Regression

1. Prediction: Using a hypothesis function, predictions are made based on input features.
2. Evaluation: Implements a cost function, "mean squared error," to measure prediction accuracy.
3. Learning: Employs gradient descent, which uses calculus to adjust and minimize error by updating weights and biases.

Concepts Explored

• Univariate vs. Multivariate Linear Regression: Focus on a single predictive feature versus multiple features, respectively.
• Gradient Descent: An optimization technique that iteratively updates parameters to minimize the cost function.
• Bias Parameter: Represents an average outcome in absence of specific feature information.
• Mean Squared Error: Common cost function used to quantify the error in predictions.

Resources

• Andrew Ng's Coursera Course: A highly recommended resource for comprehensive and practical learning in machine learning. Course covers numerous foundational topics, including linear regression and more advanced techniques.

Access to Andrew Ng's Course on Coursera is encouraged to gain in-depth understanding and application skills in machine learning.

Coursera: Machine Learning by Andrew Ng

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Show notes at ocdevel.com/mlg/4

The AI Hierarchy

• Artificial Intelligence is divided into subfields such as reasoning, planning, and learning.
• Machine Learning is the learning subfield of AI.
• Machine learning consists of three phases:
  1. Predict (Infer)
  2. Error (Loss)
  3. Train (Learn)

Core Intuition

• An algorithm makes a prediction.
• An error function evaluates how wrong the prediction was.
• The model adjusts its internal weights (training) to improve.

Example: House Price Prediction

• Input: Spreadsheet with features like bedrooms, bathrooms, square footage, distance to downtown.
• Output: Predicted price.
• The algorithm iterates over data, learns patterns, and creates a model.
• A model = algorithm + learned weights.
• Features = individual columns used for prediction.
• Weights = coefficients applied to each feature.
• The process mimics algebra: rows = equations, entire spreadsheet = matrix.
• Training adjusts weights to minimize error.

Feature Types

• Numerical: e.g., number of bedrooms.
• Nominal (Categorical): e.g., yes/no for downtown location.
• Feature engineering can involve transforming raw inputs into more usable formats.

Linear Algebra Connection

• Machine learning uses linear algebra to process data matrices.
• Each row is an equation; training solves for best-fit weights across the matrix.

Categories of Machine Learning 1. Supervised Learning

• Algorithm is explicitly trained with labeled data (e.g., price of a house).
• Examples:
  • Regression (predicting a number): linear regression
  • Classification (predicting a label): logistic regression

2. Unsupervised Learning

• No labels are given; the algorithm finds structure in the data.
• Common task: clustering (e.g., user segmentation for ads).
• Learns patterns without predefined classes.

3. Reinforcement Learning

• Agent takes actions in an environment to maximize cumulative reward.
• Example: mouse in a maze trying to find cheese.
• Includes rewards (+points for cheese) and penalties (–points for failure or time).
• Learns policies for optimal behavior.
• Algorithms: Deep Q-Networks, policy optimization.
• Used in games, robotics, and real-time decision systems.

Terminology Recap

• Algorithm: Code that defines a learning strategy (e.g., linear regression).
• Model: Algorithm + learned weights (trained state).
• Features: Input variables (columns).
• Weights: Coefficients learned for each feature.
• Matrix: Tabular representation of input data.

Learning Path and Structure

• Machine learning is a subfield of AI.
• Machine learning itself splits into:
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
• Each category includes multiple algorithms.

Resources

• MachineLearningMastery.com: Accessible articles on ML basics.
• The Master Algorithm by Pedro Domingos: Introductory audio-accessible book on ML.
• Podcast’s own curated learning paths: ocdevel.com/mlg/resources

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Show notes at ocdevel.com/mlg/3. 

This episode covers four major philosophical topics related to artificial intelligence. The purpose is to give broader context to why AI matters, before moving into technical details in later episodes.

1. Economic Automation

AI is automating not just simple tasks like data entry or tax prep, but also high-skill jobs such as medical diagnostics, surgery, and creative work like design, music, and art. There are two common reactions:

• Fear: Concern over job displacement, similar to past economic shifts like the agricultural and industrial revolutions.
• Is your job safe?
• Optimism: Automation may lead to more comfortable living conditions and economic structures like Universal Basic Income. New job types could emerge, as they have in past transitions.

2. The Singularity

The singularity refers to a point of runaway technological growth, where AI becomes capable of improving itself recursively. This concept is tied to "artificial general intelligence" and "seed AI"—systems that not only perform tasks but create better versions of themselves. The idea is that this could trigger extremely rapid change, possibly representing a new phase of evolution beyond humanity.

3. Consciousness

I explore whether consciousness can emerge from machines. Since the brain is a physical machine and consciousness arises from it, it's possible that artificial systems could develop similar properties. Related ideas:

• Qualia: Subjective experiences.
• Functionalism: If something behaves like it’s conscious, it may be conscious.
• Turing Test: If a machine is indistinguishable from a human in conversation, it passes the test.

4. Misaligned Goals and Risk

I discuss scenarios where AI causes harm not through malevolence but through poorly defined objectives. One example is the "paperclip maximizer" thought experiment, where an AI tasked with maximizing paperclip production might consume all resources to do so. This has led some public figures to raise concerns about AI safety. I don't share the same level of concern, but the topic is worth being aware of.

References

• Ray Kurzweil, The Singularity is Near
• Ray Kurzweil, How to Create a Mind
• Daniel Dennett, Consciousness Explained
• Nick Bostrom, Superintelligence
• The Great Courses, Philosophy of Mind, Brain, Consciousness, and Thinking Machines

In the next episode, I begin covering the technical foundations of machine learning, starting with supervised, unsupervised, and reinforcement learning.

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Show notes at ocdevel.com/mlg/2

Updated! Skip to [00:29:36] for Data Science (new content) if you've already heard this episode.

What is artificial intelligence, machine learning, and data science? What are their differences? AI history.

Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions.

Artificial Intelligence (AI) - Wikipedia

Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AlphaGo Movie, very good!

Sub-disciplines

• Reasoning, problem solving
• Knowledge representation
• Planning
• Learning
• Natural language processing
• Perception
• Motion and manipulation
• Social intelligence
• General intelligence

Applications

• Autonomous vehicles (drones, self-driving cars)
• Medical diagnosis
• Creating art (such as poetry)
• Proving mathematical theorems
• Playing games (such as Chess or Go)
• Search engines
• Online assistants (such as Siri)
• Image recognition in photographs
• Spam filtering
• Prediction of judicial decisions
• Targeting online advertisements

Machine Learning (ML) - Wikipedia

Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Data Science (DS) - Wikipedia

Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

History

• Greek mythology, Golums
• First attempt: Ramon Lull, 13th century
• Davinci's walking animals
• Descartes, Leibniz

• 1700s-1800s: Statistics & Mathematical decision making

  • Thomas Bayes: reasoning about the probability of events
  • George Boole: logical reasoning / binary algebra
  • Gottlob Frege: Propositional logic
• 1832: Charles Babbage & Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines

• 1936: Universal Turing Machine

  • Computing Machinery and Intelligence - explored AI!
• 1946: John von Neumann Universal Computing Machine
• 1943: Warren McCulloch & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> neural networks by way of MLP)

• 50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon

  • Newell & Simon: Hueristics -> Logic Theories, General Problem Solver
  • Slefridge: Computer Vision
  • NLP
  • Stanford Research Institute: Shakey
  • Feigenbaum: Expert systems
  • GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems
• 70s: Lighthill report (James Lighthill), big promises -> AI Winter

• 90s: Data, Computation, Practical Application -> AI back (90s)

  • Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation
• Bloomberg, 2015 was whopper for AI in industry
• AlphaGo & DeepMind

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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