Machine Learning Guide

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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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Checkpoint - learn the material offline! ocdevel.com/mlg/17 for notes and resources

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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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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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Your first classifier: Logistic Regression. That plus Linear Regression, and you're a 101 supervised learner! ocdevel.com/mlg/7 for notes and resources

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Discussion on certificates and degrees from Udacity to a Masters degree. ocdevel.com/mlg/6 for notes and resources

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Introduction to the first machine-learning algorithm, the 'hello world' of supervised learning - Linear Regression ocdevel.com/mlg/5 for notes and resources

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Overview of machine learning algorithms. Infer/predict, error/loss, train/learn. Supervised, unsupervised, reinforcement learning. ocdevel.com/mlg/4 for notes and resources

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

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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.

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

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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