Machine Learning Guide

Dept

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. read less

MLG 001 Introduction
Feb 1 2017
MLG 001 Introduction
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 GuideDept AgencyGnothi (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 fundamentalsAspiring 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 machinesMisc Topics: Linear Digressions, Data Skeptic, Learning machines 101iTunesU issues Planned episodes What is AI/ML: definition, comparison, historyInspiration: automation, singularity, consciousnessML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applicationsMath overview: linear algebra, statistics, calculusLinear models: supervised (regression, classification); unsupervisedParts: regularization, performance evaluation, dimensionality reduction, etcDeep 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
MLG 002 What is AI, ML, DS
Feb 9 2017
MLG 002 What is AI, ML, DS
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 solvingKnowledge representationPlanningLearningNatural language processingPerceptionMotion and manipulationSocial intelligenceGeneral intelligence Applications Autonomous vehicles (drones, self-driving cars)Medical diagnosisCreating art (such as poetry)Proving mathematical theoremsPlaying games (such as Chess or Go)Search enginesOnline assistants (such as Siri)Image recognition in photographsSpam filteringPrediction of judicial decisionsTargeting 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, GolumsFirst attempt: Ramon Lull, 13th centuryDavinci's walking animalsDescartes, Leibniz 1700s-1800s: Statistics & Mathematical decision making Thomas Bayes: reasoning about the probability of eventsGeorge Boole: logical reasoning / binary algebraGottlob 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 Machine1943: 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 SolverSlefridge: Computer VisionNLPStanford Research Institute: ShakeyFeigenbaum: Expert systemsGOFAI / 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 industryAlphaGo & DeepMind