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#1: Components of a Learning System

    Sep 2, 2021 06:34 PM

    Components of a Learning System

    notion image

    Example: Credit Approval System

    Input: Customer Information (demographics, income, age, gender)
    Output: Binary credit decision (made money or not)
    Unknow target function (Idea credit approval formula)
    Training Data (histoical records of current customers)
    • typically I.I.D (independent & identically distributed)
    Learning Algorithm → Final Hypothesis (Learned credit approval func)
    Hypothesis Set (Condidate predictive functions)

    A Simple Learning Model

    Hypothesis Set: All linear functions

    Weight w(t)

    Assuming data linearly seperable: this algoritm h(x) works


    out-of-sample error (unknown)
    in-sample error (training error - known)

    Hypothesis Space

    The set of functions that includes g that best approximates f
    Picking a hypothesis space = selecting the type of algo / modal to use

    Complexity of

    • Complex : better chance of approximating in-sample
    • Simple : better chance of generalizing out-of-sample

    Goal of Machine Learning

    Minimize given

    Learning Paradigms

    Supervised Learning

    Explicitly provided w/ labeled inputs

    Online Learning

    Does not have access to all data upfront
    Given the algo one example at a time
    e.g. auto driving

    Active Learning

    Algo allowed to query egs for its training

    Unsupervised Learning

    Only given input examples w/o annotations
    • Like human's obsersational learning
    • A hard problem


    • Clustering Images
    • Embedding Images: give each images an coord to put similar ones together
    • Embedding Words

    Semi-Supervised Learning

    Only given a subset of labels


    Label propagation

    Self-Supervised Learning

    Infer labels from large collections of unlabeled data by exploiting other properties associated w/ dataset
    • Professor Worked on it during PHD

    Why Works

    Supervised: hitting its limit
    Unsupervised: too hard to approach


    Exploiting temporal correlatons of video frames to deduce that image from nearby frames are similar

    Reinforcement Learning

    Agent finds its own way & use positive/negative reward/punishment for guide

    Course Overview

    1. ML Overview: learning & generalization
    1. Probabilistic approach
    1. Linear Parametric models
    1. Non-Linear Parametric models
    1. Unsupervised

    TODO: Homework 0

    Content: questions from probability, calculus, linear algebra (10 questions)
    On Brightspace