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MAST30034 Applied Data Science
创建日期：2024-05-31 16:22:47
所属分类：数学mathematics
标签： MAST30034
Applied Data Science

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MAST30034 Applied Data Science

Outline
• Administrative Information
• Basics of Descriptive Statistics
• Introduction to Machine Learning
• Notions of Prediction
• Supervised and Unsupervised Learning
• Notions of Complexity
Administrative Information
Teaching staff
Administrative Information
Subject structure
• 1 hour lecture per week: Tuesday 11:00 to 12:00 in
PAR-Elisabeth Murdoch G06
• 2 hour tutorial per week and 9 groups
• Tutorials will be provided for the first 5 weeks but will only
cover a portion of time. Weeks 6 to 12 will be for Project 2
• Remaining time is intended for use on the assessments or to go
through advanced learning content
• Monday: G4. 09:00-11:00, G1. 13:00-15:00 and G3.
15:15-17:15 (PAR-Peter Hall-212, Nanson Laboratory),
• Tuesday: G6. 15:15-17:15 (PAR-Peter Hall-G69, Thompson
Lab),
• Wednesday: G9. 11:00-13:00 (PAR-Peter Hall-G70 Wilson
Laboratory),
• Thursday: G2. 09:00-11:00 (PAR-Peter Hall-G70 Wilson
Laboratory),
• Friday: G7. 10:00-12:00, G8. 12:00-14:00 and G5.
14:15-16:15 (PAR-Peter Hall-212, Nanson Laboratory).
Administrative Information
Ongoing assessment
• 1 Individual Projects and two quizzes (30% + 10%)
• 1 Group Project (50%)
• 1 Team Review (10%)
Administrative Information
Subject pre-requisites and assumed knowledge
• Python and/or R
• Git (revision will be provided)
• LaTeX (revision guide will be provided)
• Any content covered in COMP30027, MAST30035,
MAST20005, COMP20008
New tools
• Apache Spark 3.0 Framework (PySpark, dplyR/sparklyR)
• Apache Arrow Framework
• Geospatial Plotting libraries
• JupyterHub Server (bash, git)
Administrative Information
Project 1, quantitative analysis
• Already released, we recommend you start as soon as possible
if you have yet to do so
• Worth 30% of your final grade due on the 13th of August at
11:59 am AEST (end of week 3)
• Requirements: Submit a GitHub repository and report written
in LaTeX
• More covered in your tutorials this week
• LPT: Start as soon as possible
Administrative Information
Assignment 1, two set of quizzes
• Releases in Week 4 to 6
• Worth 10% of your final grade due soon after released
• Multiple choice questions
• The assignment will consist of questions related to the
content taught in lectures 2 to 6
Administrative Information
Project 2, industry project
• Group Project starting Week 5 or 6 until Week 12
• Worth 50% of your final grade, though marks may be scaled
within group members depending on the Team Reviews
• Groups must consist of 5 members and can be from any
stream or tutorial delivery mode. However, you must find a
suitable tutorial time to attend as Group Workshop
Attendance is compulsory from Week 6 to 12
• There will be 2 to 3 projects to choose from; provided by our
Industry Partners and/or from the University. There will be
limited spots
• Groups may need to sign Non-Disclosure Agreements (NDAs).
If you are unable to sign it, then you should choose the
University provided project
• More information covered in Workshop 5/Lecture 6
Administrative Information
Project 2, team review
• A short Team Review and Self-Reflection due at the end of
SWOTVAC (as there is no exam)
• Worth 10% of your final grade and will be your chance to rate
yourself and your team members based on contribution and
work effort
• Depending on the ratings, we may scale some group members
up and down to ensure an equal amount of work has been
done by all group members
• More covered closer to the date
Administrative Information
BYOD vs JupyterHub
• This year, we will be running a JupyterHub server supporting
Python 3.8.3 and R 4.0.3 via Jupyter Notebooks or R-Studio
• It is strongly recommended students use their own devices if
they have moderate specs (i.e 8-16GB of RAM, an i5 or
equivalent, an external GPU if applicable)
• Access: https://mast30034.science.unimelb.edu.au/hub/login
• Log in using your UniMelb credentials (read more on the
Canvas homepage)
Basics of Descriptive Statistics
Statistics is a branch of mathematics dealing with the collection,
organization, analysis, interpretation and presentation of data,
which enable us to
• accurately describe the
finding of scientific research,
• make decisions
• make estimation/prediction
Basics of Descriptive Statistics
Vector Data: is a vector of the same data type, each entry is an
observation, the observations can be independent or dependent.
Rectangular Data: is the typical frame of reference for an analysis
in data science is a rectangular data object, like a spreadsheet or
database table. The key terms for rectangular data are
• Feature: A column in the table is commonly referred to as
feature.
Synonyms: attribute, input, predictor, variable.
• Outcome: many science projects involves predicting an
outcome, often yes/no response or a summarized term.
Synonyms: dependent variable, response, target, outcome.
• Record: A row in the table is commonly referred as a record.
Synonyms: case, example, instance, observation, pattern,
sample.
Basics of Descriptive Statistics
Some commonly seen data type:
• Continuous: “trip distance”, “trip amount”.
• Discrete: usually integer, “passenger count” (Both continuous
and discrete data are numerical.)
• Categorical: “blood type”
Basics of Descriptive Statistics
Descriptive statistics: allow us to characterize the data based on
its properties. There are four major types of descriptive statistics:
• Measures of frequency
• Measures of central tendency
• Measures of dispersion or variation
• Measures of association
Basics of Descriptive Statistics
Frequency: To be used when you want to show how often a
response is given or show how often something occurs.
Central tendency: Mean, Median, and Mode
• Mean: the sum of a variables values divided by the total
number of values.
• Median: the middle value of a variable.
• Mode: the value that occurs most often
Use this when you want to show how an average or the most
commonly indicated response.
Basics of Descriptive Statistics
Dispersion or variation:
• Range = maximum - minimum.
• Variance or Standard Deviation: the difference between
observed score and mean.
Use this when you want to show how spread out the data are. It is
helpful to know when your data are so spread out that it affects
the mean.
Basics of Descriptive Statistics
Measures of association between two variables: Covariance and
Correlation
• A correlation coefficient is used to measure the strength of the
relationship between numerical variables. The most common
correlation coefficient is Pearson correlation coefficient, which
can range from -1 to +1.
• Scatter plots can be useful (display the relationship between
two quantitative or numeric variables by plotting one variable
against the value of another variable).
Basics of Descriptive Statistics
Measures of association between multiple variables: Correlation
matrix, Covariance matrix:
• A covariance (or correlation) matrix is a matrix with its j , kth
entry equals to the covariance (or correlation) between
variable j and k .
Cov(trip dis, fare, tips)
 1 0.89 0.510.89 1 0.54
0.51 0.54 1
 ,
• Principle Component Analysis (PCA): PCA is a method to
represent the original dependent (observed) variables using
some new independent (latent) variables.
Basics of Descriptive Statistics
Model: Input → output Full model or reduced model? For a large
number of predictors (features/variables), we would like to identify
the ones that exhibit the strongest effects to the response.
Basics of Descriptive Statistics
Some of the characteristics we might want to report of a dataset:
• Do the values tend to cluster around a particular point?
• Is there more than one cluster?
• How much variability is there in the values?
• How quickly do the probabilities drop of as we move away
from the modes?
• Outliers: are there extreme values far from the modes?
Descriptive statistics are just descriptive. They do not involve
generalizing beyond the data at hand. Generalizing from our data
to another set of cases is the business of inferential statistics.
Introduction to Machine Learning
• The wakeful human mind is constantly acquiring sensorial
information from the environment, in the form of vision,
hearing, smell, touch, and taste signals.
• The human mind is the best learning system there is to
process these kind of data, in the sense that no computer as
yet can consistently outperform a rested and motivated person
in recognizing images, sounds, smells, and so on.
• Machine Learning applications in fields such as computer
vision, robotics, speech recognition and natural language
processing, generally have as their goal to emulate and
approach human performance as closely as possible.
Introduction to Machine Learning
• Pattern recognition is one of the first engineering field in
which AI problems were addressed. It dates back to 1960’s.
• Machine Learning originated mainly in the neuroscience and
computer science areas.
• Other identifiable areas closely related to this topic are
Artificial Intelligence, Data Science and Signal and Image
Processing.
Introduction to Machine Learning
• In supervised learning, information about the problem is
summarized into a vector of measurements x ∈ Rd also known
as a feature vector, and a target y ∈ R to be predicted.
• The relationship between the feature vector x and the target y
is, in practice, rarely deterministic, i.e., there is no function f
such that y = f (x).
• Instead, the relationship between x and y is better described
by a joint probability distribution px,y .
• This state of uncertainty is due mainly to the presence of:
• latent factors on which y depends but that are not observed or
measured
• measurement noise
Introduction to Machine Learning
Stochastic relationship between the features and target in
supervised learning
Prediction
• A prediction rule or relationship produces a predictor
ψ : Rd → R, such that ψ(x) predicts y . The predictor itself is
not random.
• The construction of the predictor ψ uses information about
the joint distribution px,y which can be
• direct knowledge about px,y
• indirect knowledge about px,y through a set of independent
and identically distributed (i.i.d) sample
D = {(x1, y1) , ..., (xn, yn)}, often called the training data
• The development of a predictor will employ a combination of
these two sources of information.
Optimal vs. Data-Driven Predictors
• An optimal predictor ψ∗ (x) can be obtained in the case of
complete knowledge of px,y is available.
• Alternatively, a data-driven prediction rule must rely solely on
D.
• The optimal predictor can be obtained as n→∞ under
certain conditions. The rate of convergence can however be
slow.
• In the finite n case, some a priori knowledge about px,y will
help achieve good performance.
Predictor
• The performance and the development of a predictor is
obtained using a loss function ` : R× R→ R. Examples
include
• the quadratique loss ` (ψ(x), y) = (y − ψ(x))2
• the absolute difference loss ` (ψ(x), y) = |y − ψ(x)|
• the missclassification loss
` (ψ(x), y) = Iy 6=ψ(x) =
{
1, y 6= ψ(x),
0, y = ψ(x)
where Iα = 1 if α is true and Iα = 0, otherwise.
• While we have access to a training data set, our interest is in
the expected loss of ψ defined as
L [ψ] = E [` (ψ(x), y)] .
The optimal predictor ψ∗ minimizes L [ψ] over all possibles
ψ ∈ P, where P is the class of all predictors under
consideration.
Supervised and Unsupervised Learning
• In supervised learning, the response y is available and defined.
There are two types of supervised learning problems.
• Classification where y ∈ {0, 1, ...,K − 1} takes values in a
finite alphabet, K is the number of classes. The variable y is
in this case called a label to emphasize that it has no
numerical meaning.
• in binary classification, there are two classes and K = 2.
• The used loss for classification is the missclassification loss
with
[ψ] = E
[
Iy 6=ψ(x)
]
= P (y 6= ψ(x))
In binary classification Iy 6=ψ(x) = |y − ψ(x)| or (y − ψ(x))2,
they all yield the classification error [ψ].
Supervised and Unsupervised Learning
• Regression where y ∈ R, and the quadratic loss is a common
used loss function and the prediction error L [ψ] is called the
mean-square error.
• Examples of regression include linear regression
• In unsupervised learning, y is not defined or given so only
the distribution px is used.
• Examples of unsupervised learning operations include Principal
Component Analysis (PCA).
Notions of Complexity
• Complexity trade-offs involving sample size, dimensionality
and empirical performance. It is a characteristic feature of
supervised learning methods.
• Notion of curse of dimensionality : for a fixed sample size, the
expected classification error will improve by increasing the
number of features, but eventually will decrease. This is a
consequence of the large size of high-dimensional spaces,
which require correspondingly large training sample sizes.
• Scissors effect: the expected error typically decreases as
sample size increases, and more complex classification rules
achieve smaller error for large sample sizes; however, simpler
classification rules can perform better under small sample
sizes, by virtue of needing less data.
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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 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