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# INTRODUCTION TO MACHINE LEARNING Assignment #2 solution

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CS    489/689
INTRODUCTION    TO    MACHINE    LEARNING

Assignment    #2

Description:
Ø For    this    assignment,    you    will    implement two linear    regression    algorithms in
Python,    Julia,    or    MATLAB    to    solve    a    regression problem. You    may use    any    and    all
built-in    functions    including    linear    regression functions.
Ø Ordinary    least    squares    (OLS)    solution:    Implement    the    ordinary    least    squares
solution    as    discussed    in    class.

Category:

## Description

CS    489/689
INTRODUCTION    TO    MACHINE    LEARNING

Assignment    #2

Description:
Ø For    this    assignment,    you    will    implement two linear    regression    algorithms in
Python,    Julia,    or    MATLAB    to    solve    a    regression problem. You    may use    any    and    all
built-in    functions    including    linear    regression functions.
Ø Ordinary    least    squares    (OLS)    solution:    Implement    the    ordinary    least    squares
solution    as    discussed    in    class.
regression    as    discussed    in    class.
Ø For    both    algorithms,    don’t    forget    to    include    the    bias    term    in    the    parameter    vector.
Ø Train    and    test    your    model    with    a    dataset    of    your    choosing    that    meets    the    following
criteria:
o Number    of    features:     3+
o Feature    characteristics:     Real-valued
o Output    characteristics:     Real-valued
Ø Use    80%    of    the    dataset    for    training    and    20%    for    testing    your    model.
Ø If    you’d    like,    you    may    use    one    of    the    following    sources    to    find    a    dataset:
o University    of    California,    Irvine    Machine    Learning    Repository
https://archive.ics.uci.edu/ml/index.php
o Kaggle    https://www.kaggle.com/
o Awesome    Public    Datasets    https://github.com/awesomedata/awesome-publicdatasets
o Microsoft    Research    Open    Data    https://msropendata.com/
o U.S.    Government’s    Open    Data    https://www.data.gov/
o Registry    of    Research    Data    Repositories    https://www.re3data.org/
o CMU    Libraries    https://guides.library.cmu.edu/machine-learning/datasets
Ø Summarize    your    approach    and    results    in    a    report    that    includes    at    least    the    following:
o The    dataset    you    used,    its    source    and    characteristics.
o The    data    preprocessing    steps    you took (if    any).
o The    solution    �” for    both    algorithms.
o The    learning    rate(s)    you    used    for    gradient    descent    and    how    many    iterations    it
took    for    gradient    descent    to    converge.
o Relevant    evaluation    metrics    for    the    training    dataset for both    algorithms.
o Relevant    evaluation    metrics    for    the    test    dataset with    for both    algorithms.
o Any    additional    details    you    would    like    to    include.
Ø Submit    your    report    along    with    your    dataset    and    source    code. Feel    free    to    include    your
code    in    the    report,    but    you    also    need    to    submit    your    source    code    files    (.py,    .jl,    or    .m)
and    your    dataset    separately,    so    that    your    results    can    be    replicated for    scoring.
Submission    Instructions:
Compress    all    the    files    and    name    the    submission    file <YourLastName_Assignment2:
Ø If you    are    completing    the    assignment    individually,    your    last    name    is    Smith,    and    you
are    submitting    a    .zip    file,    the    file    should    be    named    Smith_Assignment2.zip.
Ø If    you    are    completing    the    assignment    as    a    team    of    two,    your    last    names    are    Rogers
and    Smith,    and    you    are    submitting    a    .zip    file,    the    file    should    be    named
Rogers_Smith_Assignment2.zip. Only    one    of    the    team    members    needs    to    submit    the
assignment.