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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

• "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

Support my new podcast: Lefnire's Life Hacks

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.

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

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.

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.

• 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

Support my new podcast: Lefnire's Life Hacks

Show notes at ocdevel.com/mlg/3. Why should you care about AI? Inspirational topics about economic revolution, the singularity, consciousness, and fear.