HI.
THANKS FOR COMING TO THE
PRESENTATION, THE LECTURE.
MY NAME IS KEN CHEUNG, I'M A
PROFESSOR AT COLUMBIA UNIVERSITY
AND I'VE BEEN WORK WITH NIENDZ
FOR QUITE A
WORKING WITH NINDS FOR QUITE A
WHILE, AND TODAY I'LL TALK ABOUT
MACHINE LEARNING?
MEDICAL TRIALS AS WELL AS
BIOMEDICAL RESEARCH, SO I THINK
WE HAVE TIME FOR QUESTIONS
TOWARDS THE END, BUT IF YOU HAVE
QUESTIONS, YOU CAN SEND IN THE
CHAT BOX.
OKAY.
SO THIS IS A VERY BRIEF AGENDA.
SO I'M GOING TO START WITH A
VERY, VERY BRIEF OVERVIEW OF
MACHINE LEARNING, AND I'M GOING
TO FOCUS ON TWO SPECIFIC TYPES
OF MACHINE LEARNING, SUPERVISED
LEARNING AND UNSUPERVISED
LEARNING.
HOPEFULLY BY NOW PEOPLE HAVE
HEARD OF THE WORD ART OFFICIAL
INTELLIGENCE.
MACHINE LEARNING IS A SUBFIELD
OF ARTIFICIAL INTELLIGENCE WITH
A SPECIFIC STATED TO DO
PREDICTION, TO MAKE
RECOMMENDATION, FOR PATTERN
RECOGNITION, DIMENSION REDUCTION
AND THERE ARE A LOT OF
APPLICATIONS, ROBOTICS AND
GAMING, THE BASIC AREAS THAT
MACHINE LEARNING CAN BE APPLIED
TO.
OBVIOUSLY I'M GOING TO TALK
ABOUT APPLICATIONS AND AREAS
THAT ARE RELEVANT TO THE
BIOMEDICAL RESEARCH AREA.
BUT FIRST I'M GOING TO TALK
ABOUT DIFFERENT TYPES OF MACHINE
LEARNING.
THE FIRST TYPE IN MACHINE
LEARNING IS CALLED SUPERVISED
LEARNING.
THE IDEA OF SUPERVISED LEARNING
IS THAT WE'RE GOING TO TRAIN
USING DATA TO TRAIN AN ALGORITHM
USING DATA THAT IS IN THE FORM
OF FEATURES SOMETIMES CALLED
PREDICTORS, AND WITH A KNOWN
OUTCOME, SOMETIMES CALLED
OUTCOMES.
I'LL GIVE YOU AN EXAMPLE WHEN I
GET TO THAT SECTION OF
SUPERVISED LEARNING, BUT THE
IDEA IS THAT THE TRAINING IS
SUPERVISED BY SOME GROUND TRUTH
AND BASED ON THE TRAINED
ALGORITHM, WE COULD USE THE
ALGORITHM FOR PREDICTION
PURPOSES, THAT IS, WE CAN
PREDICT AN OUTCOME, PREDICT WHAT
WOULD HAPPEN WITH DIFFERENT
INPUT AND SOME FEATURES.
SO THE FIRST GOAL OF SUPERVISED
LEARNING IS TO DO PREDICTION,
AND THAT'S WHAT WE CALL ONE OF
THE INFERENCING GOALS.
ANOTHER INFERENCING GOAL IS TO
MAKE RECOMMENDATION, THAT IS,
MAKE A PREDICTION BASED ON WHAT
IS BEING INPUT TO THE SYSTEM
WITH PREDICTION, AND THEN BASED
ON PREDICTION, WHAT WE SHOULD
DO.
THAT WILL BE THE RECOMMENDATION
PART.
SO ON THE DIAGRAM TO YOUR RIGHT,
YOU SEE THIS IS A DIAGRAM FROM
THE DATA, THE ALGORITHM WE USE
WHAT WE CALL TRIMMING DATA TO
COME UP WITH AN ALGORITHM AND
THEN INFERENCE AND
RECOMMENDATION.
THIS IS SOME OF THE SUPERVISED
LEARNING PARADIGM.
IN CONTRAST WITH SUPERVISED
LEARNING, ANOTHER APPROACH IS
CALLED UNSUPERVISED LEARNING.
A DIFFERENCE BETWEEN SUPERVISED
AND UNSUPERVISED LEARNING IS
THAT IN UNSUPERVISED LEARNING
WE'RE GOING TO TRAIN THE
ALGORITHM USING THE FEATURES
ONLY.
FOR EXAMPLE, IN SUPERVISED
LEARNING WE MAY TRY TO GIVE AN
IMAGE OF A CAT AND GIVE YOU ALL
THE FEE TUFERS A CAT AND THEN
THE ALGORITHM WOULD TRAIN ON
THESE FEATURES SO YOU CAN
PREDICT THAT WHENEVER YOU GIVE
THAT IMAGE, YOU PREDICT IT'S A
CAT.
IN UNSUPERVISED LEARNING, WE'RE
NOT TELLING THE ALGORITHM THAT
THIS IS A CAT.
WE JUST GIVE THE ALGORITHM A
BUNCH OF FEATURES AND THEN TO
TRY TO LEARN ABOUT WHAT THE
PATTERN AND THE FEA TOURS.
AS A RESULT OF THAT -- FEATURES.
AS A RESULT OF THAT, THE MAIN
INFERENCE WE CAN DRAW FROM
UNSUPERVISED LEARNING IS TO
IDENTIFY HOMOGENOUS POPULATIONS
BASED ON THE FEATURES AND ALSO
IN SITUATIONS WHEN WE HAVE A LOT
OF FEATURES AND INPUTS, WE WANT
TO REDUCE THE SET TO A MORE
MANAGEABLE NUMBER OF FEATURES,
THEN WE DO DIMENSION REDUCTION.
SO THE TWO MAIN GOALS IN
UNSUPERVISED LEARNING WOULD BE
TO IDENTIFY HOMOGENOUS
SUBPOPULATIONS AND TO DO
DIMENSION REDUCTION.
AGAIN I'M GOING TO GIVE YOU
EXAMPLES, SPECIFIC EXAMPLES AND
USE CASES ON THESE TWO TOP
LEARNINGS AS WE MOVE ALONG.
THE THIRD TYPE OF LEARNING
METHOD IS WHAT WE CALL ONLINE
LEARNING.
SO THE IDEA IS THAT THE DIAGRAM
ON THE SLIDE, YOU SEE THAT
THERE'S AN ARROW ON THE UPPER
PART.
THAT IS WE'RE GOING TO UPDATE A
TRAINING, UPDATE DATA, COLLECTED
FROM A SYSTEM, AND THEN USE THE
NEW DATA TO UPDATE THE
ALGORITHM.
SO THIS IS REALLY THE BASIC
CONCEPT OF ONLINE LEARNING IS
THAT ONCE WE TRAIN THE
ALGORITHM, IT WILL BE EVOLVING
AND YOU'LL KEEP LEARNING.
THE IDEA IS THAT ONCE IT'S
DEPLOYED IN A RECOMMENDED
SYSTEM, WE COLLECT NEW DATA AND
THEN WE USE THE NEW DATA TO GO
BACK TO THE TRAINING MODE AND TO
IMPROVE THE QUALITY OF THE
PREDICTION AS WELL AS THE
QUALITY OF THE RECOMMENDATION.
AND IN THE CONTEXT OF HUMAN
TRIALS, IT'S ACTUALLY ONE OF THE
SPECIFIC FORMS OF ONLINE
LEARNING.
SO IN TRIALS WHERE WE ENROLL A
FEW PARTICIPANTS AT THE TIME AND
THEN FROM WHAT WE LEARN FROM THE
PATIENT PARTICIPANTS, WE DECIDE
ON WHETHER WE WANT TO CHANGE THE
DOSE OF A TREATMENT, WE WANT TO
CELLS THE DURATION OF A
TREATMENT, WE WANT TO CHANGE THE
ENDPOINT OF A TREATMENT, THIS IS
THE DECISION THAT THE BASIS
POINT DURING THE STUDY.
SO THIS IS ONE FORM OF ONLINE
LEARNING.
SO THIS IS SOMETHING THAT WE'RE
FAMILIAR WITH.
THE FOURTH TYPE OF LEARNING IS
CALLED REINFORCEMENT LEARNING.
THE IDEA IS THAT WE SEND AN
AGENT TO THE FIELD AND THEN
BASED ON THE FEATURES AND THE
FEEDBACK, WE'RE GOING TO SEND IT
BACK TO THE AGENT, AND THEN
BASED ON THAT, WE GIVE THE AGENT
SOME SIGNAL ABOUT THIS IS THE
RIGHT DECISION OR WRONG
DECISION, THE FEEDBACK WILL HELP
THE AGENT TO LEARN.
SO THIS IS A TYPE OF LEARNING
METHOD THAT IS USED A LOT IN
ROBOTICS TRAINING, HOW DO YOU DO
A TASK TO TRAIN WHETHER THEY
KNOW ONE TASK THAT THEY DO IS
CORRECT OR NOT.
SO THIS IS SOMETHING THAT IS
KIND OF -- IS A VERY INTERESTING
APPLICATION.
ANOTHER APPLICATION IS TRAINING
AN ALPHA GOAL TO PLAY CHESS,
THOSE THAT HAVE HEARD ON THE
NEWS, ALPHA GOAL IS TRAINING
BASED ON LEARNING.
BACK TO HUMAN TRIALS, WE HAVE
ACTUALLY USED IT A LOT IN
CLINICAL MANAGEMENT OF PATIENTS.
SO WHEN A PATIENT COMES IN WITH
CERTAIN DIAGNOSIS, WE USUALLY
TREAT THEM WITH SOME SORT OF
PERSONALIZED TREATMENT BASED ON
RECOMMENDATIONS OR APPROVED DRUG
BASED ON RCT.
AND IF THE FIRST RECOMMENDATION
DOESN'T WORK, THEN BASED ON THE
RESPONSE OF THE PATIENT TO
PRESCRIBE THE SECOND LINE
TREATMENT AND SO ON.
SO THIS IS IN SOME WAY
REINFORCEMENT LEARNING THAT IS
BASED ON THE SEQUENCE OF
TREATMENT AND OBSERVATION, WE
MAKE DECISION AND WE LEARN OVER
TIME WHAT WOULD BE THE BEST
SECONDARY TREATMENT IN CASE THE
FIRST LINE DOESN'T WORK.
ONE SPECIFIC SAMPLE THAT I
WORKED ON IS IN THE CONTEXT OF
MANAGING PATIENTS WITH
DEPRESSION SYMPTOMS, AND THE
IDEA IS THAT WHETHER WE GIVE
THEM MEDICATION OR GIVE THEM
BEHAVIORAL THERAPY, SO IF WE
LOOK AT THE VERY, VERY IMMEDIATE
ENDPOINT, WE HAVE DONE A STUDY
WHERE WE SHOW THAT GIVING PEOPLE
MEDICATION IMPROVES THE
SHORT-TERM SYMPTOM BETTER THAN
GIVING THEM CPT.
BUT WHAT'S INTERESTING IS THAT
IF YOU OBSERVE THE PARTICIPANT
FOR A LONGER TERM, THOSE WHO
STARTED OUT WITH CPT FIRST AND
THEN GIVEN MEDICATION END UP
BETTER THAN PARTICIPANTS
RECEIVING MEDICATION ALL THE
WAY.
AND THE REASON IS THAT THE
THEORY IS THAT CPT WILL ACTUALLY
HELP PATIENTS TO PERFORM A LOT
OF PROBLEM-SOLVING AND TO
IMPROVE THE EFFICACY AND IMPROVE
MEDICATION ADHERENCE, SO BIT
TIME THEY'RE GIVEN MEDICATION,
THEY'LL BETTER ADHERE TO THAT.
SO THIS IS ONE FORM OF THE
REINFORCEMENT LEARNING EXAMPLE
THAT WE USE A LOT IN TREATMENT
OR STAFF CARE APPROACH FOR
DEPRESSION.
SO I WON'T BE TOUCHING ON THIS
BECAUSE IT IS QUITE INVOLVING,
BUT THIS IS ONE CONCEPT THAT'S
VERY IMPORTANT IN MANAGEMENT IN
THE CLINIC FOR PATIENTS.
THE LAST TYPE OF LEARNING IS
CALLED ENSEMBLE LEARNING.
SO IT IS NOT A SPECIFIC FORM OF
LEARNING.
RATHER, THIS IS A METHOD THAT
TRIES TO COMBINE DIFFERENT
LEARNERS.
SO THE IDEA IS THAT THERE'S SO
MANY METHODS, EITHER SUPERVISED
LEARNING, UNSUPERVISED LEARNING
IN THE MARKETPLACE, AND THE IDEA
IS THAT EACH METHOD WILL HAVE
MERIT.
SO SOME METHODS ARE BETTER AT
CERTAIN DATASET THAN THE OTHER
METHOD AND VICE VERSA.
THE IDEA OF ENSEMBLE LEARNING IS
THAT INSTEAD OF USING ONLY ONE
LEARNING METHOD, WE'RE GOING TO
PULL ALL THE LEARNING METHODS
OUT THERE, SO IN ONE TRAINING
DATASET WE'LL APPLY, SAY, FIVE
DIFFERENT METHODS, AND THEN
BASED ON THE FIVE DIFFERENT
METHODS, WE'RE GOING TO PULL THE
PREDICTION INTO ONE SINGLE
SUPER-LEARNER.
SO THIS IS THE CONCEPT ON
ENSEMBLE LEARNING.
I'M GOING TO TALK TOO MUCH ABOUT
THIS BECAUSE THIS IS A LOT OF
REALLY DETAILS INTO THE
TECHNIQUE ON HOW PULLING
INFORMATION FROM DIFFERENT
ALGORITHMS.
BUT THE BASIC TERMINOLOGY THAT
YOU MAY HAVE COME ACROSS IN THE
LITERATURE WOULD BE THE BASIC
APPROACH YOU DO ON ENSEMBLE
LEARNING IS BOOSTING, TO AVERAGE
ACROSS DIFFERENT LEARNERS,
BAGGING, ANOTHER WAY OF
AVERAGING ACROSS LEARNER AND
THEN OF COURSE THIS CONCEPT
SUPER LEARNERS, WHICH IS AGAIN A
LEARNER THAT TAKES INFORMATION
FROM ALL SEPARATE LEARNING
ALGORITHMS AND THEN APPLIES THEM
INTO ONE SINGLE RECOMMENDATION.
SO THIS IS THE CONCEPT OF
ENSEMBLE LEARNING.
THERE ARE ACTUALLY MANY, MANY
MORE DIFFERENT WAYS OF AXON MY
OF MACHINE LEARNING METHODS, BUT
THESE ARE THE FIVE TYPES THAT I
SHALL INTRODUCE THE CONCEPT VERY
BRIEFLY, SUPERVISED LEARNING,
UNSUPERVISED LEARNING, ONLINE
LEARNING, REINFORCEMENT LEARNING
AND ENSEMBLE LEARNING.
SO AS I MENTIONED AT THE
BEGINNING RS I'M GOING TO FOLK
FOCUS ON SUPERVISED LEARNING AND
UNSUPERVISED LEARNING WITH
EXAMPLES TO ILLUSTRATE HOW THEY
WORK.
OKAY.
LET'S START WITH SOME USE CASES
OF SUPERVISED LEARNING.
SO THERE ARE MANY OTHER USE
CASES, BUT THESE ARE THE
EXAMPLES THAT ACTUALLY I CAME
ACROSS IN MY COLLABORATION, SO
IT'S EASIER FOR ME TO TALK ABOUT
THEM.
SO THE FIRST APPLICATION THAT WE
USE IN MACHINE LEARNING,
SUPERVISED LEARNING, RATHER, IS
TO DEVELOP AN APP RECOMMENDER
SYSTEM.
SO WE CONDUCTED A STUDY WHERE WE
LOOKED AT ABOUT 13 DIFFERENT
HEALTH APPS FOR USE WITH
DEPRESSION AND ANXIETY, SO WE
WANTED TO MANAGE THE SYMPTOMS.
AND ONE OF THE PURPOSES OF THE
RECOMMENDED SYSTEM IS TO MANAGE
THE USER EXPERIENCE AND TO HELP
THEM TO IMPROVE THE ENGAGEMENT
THROUGH THE HEALTH APPS.
SO THESE ARE SMARTPHONE APPS
THAT YOU CAN ACTUALLY DOWNLOAD
ON THE APP STORE AND GOOGLE PAY.
SO THE IDEA IS THAT WE WANT YOU
TO SEND PUSH NOTIFICATIONS TO
ENHANCE THE RESPONSE RATE OF
PARTICIPANTS SO THAT THEY START
USING APPS IN ORDER TO HELP THEM
MANAGE THE SYMPTOMS.
SO IN THIS CONTEXT, WE ARE IN A
SUPERVISED LEARNING SITUATION
WHERE IN THE SYSTEM THE FEATURE
IS GOING TO SEND THE USERS PUSH
NOTIFICATIONS WITH GIVEN
FEATURES, IN TERM OF THE TYPE OF
PUSH NOTIFICATION, THE TIMING OF
THE PUSH NOTIFICATIONSES, LIKE
WHETHER IT'S A WEEKEND, MORNING,
AFTERNOON, EVENING, AND THE
FREQUENCY OF THE PUSH
NOTIFICATIONS.
SO WE DO HAVE SET FEATURES FOR
THESE PUSH NOTIFICATIONS, AND
THEN ON THE SYSTEM WE CAN
ACTUALLY TRACK WHERE THE USER
WILL ACTUALLY RESPOND TO THE
PUSH NOTIFICATION TO OPEN AN
APP.
SO IN THIS CASE, WE WANT TO
REALLY PREDICT WHAT IS THE BEST
COMBINATION OF FEATURES OF PUSH
NOTIFICATION THAT WILL MAKE
PEOPLE TO USE THE APP.
SO THIS IS A SITUATION WHERE THE
GROUND TRUTH, WHICH IS THE
USER'S RESPONSE, AND SO THIS IS
ONE EXAMPLE THAT WE USE
SUPERVISED LEARNING FOR, SO TO
INDICATE THE OUTCOME FOR THE TWO
DIFFERENT LEARNING METHODS,
SUPERVISED LEARNING METHOD AND
TO BASED ON PREDICT RESPONSE
RATE FOR A DIFFERENT SET OF PUSH
NOTIFICATIONS.
SO THIS IS ONE OF THE EXAMPLES
WE USE SUPERVISED LEARNING.
ANOTHER EXAMPLE THAT WE ARE
CURRENTLY WORKING ON IS WE'VE
CREATED AWE PANEL OF
NEUROLOGISTS AND PHYSICAL
THERAPISTS AND PRACTITIONERS TO
CREATE AN ALGORITHM FOR PATIENTS
WHO JUST HAD A STROKE, WHETHER
THEY SHOULD BE REFERRED TO REHAB
OR WE SHOULD SEND THEM HOME.
SO THIS EXPERT PANEL PROVIDES A
LOT OF USEFUL INSIGHT IN TERMS
OF WHEN WE SHOULD SEND PATIENTS
HOME, WHEN WE SHOULD SEND THE
PATIENT TO REHAB, AND THE USUAL
COURSE IS THAT WE DON'T HAVE
EXPERTISE MANY A LOT OF LOCAL
CLINICS SO THE IDEA IS THAT
WE'RE GOING TO LEVERAGE THIS
PANEL AND ALSO PATIENT TRIALS TO
TRAIN AN ALGORITHM SO THAT THE
ALGORITHM WILL BE MAKING
RECOMMENDATIONS THAT ARE SIMILAR
TO WHAT THIS PANEL IS DOING.
SO IN THIS CASE, IT'S ALSO
SUPERVISED LEARNING BECAUSE THE
EXPERT PANEL WILL GIVE THE GOLD
DECISION OF WHAT THE RIGHT
DECISION IS IN TERMS OF SENDING
PATIENTS TO REHAB OR SENDING
THEM HOME.
THEN THE FEATURE IN THIS EXAMPLE
WILL BE THE PATIENT TRIALS AND
ALSO THE CONTEXT AND THE
BACKGROUND OF THE PATIENT.
SO THIS IS ANOTHER EXAMPLE WHERE
THE USE OF SUPERVISED LEARNING
IS VERY USEFUL.
THE THIRD EXAMPLE IS ON USING
FILE ASSAY FOR CANCER SCREENING.
SO THIS IS AN EXAMPLE THAT I'M
GOING TO SHOW YOU WITH SOME DATA
ON, BUT THE IDEA IS THAT THE
GOLD STANDARD STARTING WITH WE
HAVE A CANCER DIAGNOSIS, BUT
THIS IS NOT INVASIVE, AND IN
MANY CASES THERE'S JUST NO
RESOURCE FOR BIOPSIES, SO IN
THIS STUDY, THE GOAL IS THAT WE
WANT TO USE HPP CHANNEL ASSAYS
ASSAYSFOR SIMPLE CANCER SCREENI.
SO IN THE STUDY WE DO HAVE THE
GOLD STANDARD WHERE THERE ARE
PATIENTS WHEN WE HAVE THEIR
BIOPSY TO DETERMINE WHETHER THE
PATIENT ACTUALLY HAS CERVICAL
CANCER, AT THE SAME TIME WE
OBTAIN THE HPP CHANNELSS
AND THE LEARNING GOAL HERE IS
THAT YOU REALLY USE THE HPV
CHANNELS TO WHETHER IT'S GOING
TO BE A POSITIVE DIAGNOSIS OR
NOT.
AGAIN, WE'LL COME BACK WITH AN
EXAMPLE OF SOME DATA.
OTHER EXAMPLE INCLUDES LOOKING
AT DIFFERENT TREATMENTS, IT CAN
BE IN THE CLINICAL PHASE OR
DISCOVERY PHASE, WHERE WE HAVE
MANY MOLECULES WHERE WE WANT YOU
TO LOOK AT WHETHER THIS MOLECULE
IN COMBINATION WILL PRODUCE A
SYNERGISTIC EFFECTED AND THIS IS
THE TYPE OF SITUATION THAT AGAIN
WE CAN USE SUPERVISED LEARNING
TO DO THE PRIKDZ OF THE OUTCOME
OF THE TREATMENT COMBINATIONS --
PREDICTION OF THE OUTCOME OF THE
TREATMENT COMBINATIONS.
WE CAN APPLY SUPERVISED
LEARNING, WE HAVE LOTS OF
PATIENT FEATURES IN THE DATA, TO
PREDICT OUTCOMES.
ONE CURRENT PROJECT IS THAT WE
ARE GOING TO USE DATA FROM THE
ER, THERKS HR DATA TO PREDICT
PATIENTS ADMITTED FOR PNEUMONIA
AND TO PREDICT THE OUTCOMES,
BASICALLY TO GET THE PROGNOSIS,
PATIENTS ABOUT 200 CLINICAL
FEATURES, SO ANOTHER EXAMPLE,
VERY, VERY COMMON EXAMPLES OF
SUPERVISED LEARNING TO BUILD
CLINICAL DECISION SUPPORT
SYSTEM.
IN THE CONTEXT OF E-COMMERCE, WE
HAVE A LOT OF DATA IN TERMS OF
BUYING HISTORY, FEATURE OF THE
PRODUCTS TO PREDICT WHETHER A
CUSTOMER WILL ACTUALLY PICK A
PURCHASE.
I'M NOT GOING TO TALK MUCH ABOUT
THIS.
ONE EXAMPLE THAT MAY BE RELEVANT
TO THIS GROUP IN PARTICULAR IS
THE USE OF SUPERVISED LEARNING
TO PREDICT IMAGE.
SO THIS IS A FAIRLY
STRAIGHTFORWARD EXAMPLE.
SO WE COLLECT DATA, HANDWRITTEN
NUMBERS, AND THE IDEA IS THAT WE
WANT TO TRAIN AN ALGORITHM, WHEN
AN ALGORITHM IS PRESENTED WITH A
PICTURE OF A NUMBER, THE
ALGORITHM WILL BE ABLE TO
IDENTIFY THAT THIS IS A ZERO,
THIS IS A ONE, THIS IS A FIVE.
SO THIS IS ANOTHER APPLICATION
OF SUPERVISED LEARNING.
SO IT WAS MENTIONED THAT THIS
CAN BE APPLIED TO A SITUATION
WITH MRI DATA AND WE WANT TO
ASSOCIATE MRI THE IMAGE TO A
DISEASE OUTCOME, AND THIS IS THE
APPLICATION THAT SUPERVISED
SLEARNG VERY HELPFUL.
SO THESE ARE THE DIFFERENT --
SUPERVISED LEARNING IS VERY
HELPFUL.
SO THESE ARE THE DIFFERENT
EXAMPLES OF SUPERVISED LEARNING.
AGAIN, ONE OF THE GOALS OF
SUPERVISED LEARNING IS TO MAKE
PREDICTION.
THERE ARE TWO TYPES OF
PREDICTION THAT A SUPERVISED
LEARNER CAN DO.
ONE IS CLINICAL CLASSIFICATION.
SO THE IDEA IS THAT IT'S GOING
TO PREDICT A CATEGORICAL
OUTCOME, WHETHER IT IS A CANCER
DIAGNOSIS OR NOT, WHETHER THE
NUMBERS ARE ZERO OR FIVE,
WHETHER A USER RESPONDED TO THE
PUSH NOTIFICATION OR NOT.
SO THESE ARE CATEGORICAL
OUTCOMES THAT A SUPERVISED
LEARNER CAN DO FOR
CLASSIFICATION.
ANOTHER TYPE OF PREDICTION IS
REGRESSION.
THAT IS FOR WHEN THE GRAND TRUTH
IS A CONTINUOUS OUTCOME.
SO FOR EXAMPLE, IN THE
RECOMMENDED SYSTEM EXAMPLE --
THE APP RECOMMENDATION SYSTEM
EXAMPLE, IN ADDITION TO LOOKING
AT USERS' RESPONSE RATE, WE'RE
ALSO LOOKING AT THE TIME --
LOOKING AT THE TIME TO RESPOND
TO PUSH NOTIFICATION, SO WE WANT
TO PREDICT HOW SOON A USER WILL
RESPOND TO PUSH NOTIFICATIONS.
IN THE CONTEXT OF CLINICAL
STUDIES, WE ALSO MAY WANT TO USE
FEATURES TO PREDICT A RISK
SCORE, CATEGORY OF OUTCOMES ONCE
YOU SEE ON A SCALE OF ZERO TO
100 WHERE THE RISKS STAND ON
THIS SCALE.
SO THIS IS THE CONTEXT OF
PREDICTING CONTINUOUS OUTCOME,
SO THIS IS A REGRESSION PROBLEM.
SO THESE ARE DIFFERENT TYPES OF
SUPERVISED LEARNER.
SO THERE ARE MANY, MANY, MANY
METHODS OUT THERE.
THIS SLIDE GIVES YOU REALLY THE
MAJOR ONES IN THE LITERATURE,
THEY'RE NOT ALL OF IT, AND SO
I'M GOING TO REVEAL A FEW IN THE
NEXT FEW SLIDES, BUT THE BASIC
IDEA IS THAT THERE'S A SPECTRUM
FOR THE PRINCIPLE FOR LEARNING.
TO THE RIGHT ON THE SLIDE, SO
THESE METHODS TEND TO BE MORE
PARSIMONIOUS FROM THE STANCE
THAT WE UNDERSTAND THESE METHODS
WILL LEVERAGE A STATISTICAL
MODEL TO STRUCTURE THE DATA IN
ORDER TO MAKE PREDICTION.
THE ADVANTAGE OF USING THIS
MODEL LIKE PROVEN METHOD IS THAT
IT'S USUALLY A PRINCIPLE WAY
THAT WE UNDERSTAND WHAT THE
UNDERLYING ASSUMPTION IS SO THAT
WE CAN INTERPRET THE RESULT
FAIRLY EASILY.
SO WHEN WE USE THESE METHODS FOR
TRAINING, AFTER THE TRAINING, WE
CAN AT LEAST EXPLAIN WHY THE
ALGORITHM IS MAKING THAT
PREDICTION.
THE DISADVANTAGE OF THIS MODEL
METHOD IS OF COURSE THAT IF THE
MODEL THAT WE MAKE THE
ASSUMPTION ON IS WRONG, THE
PREDICTION CAN BE OFF.
SO THE PROS AND CONS OF USING
THE MODEL IS THAT IT'S GREAT
WHEN IT'S CORRECT, BUT IF IT'S
INCORRECT, WE MAY NOT BE MAKING
THE RIGHT PREDICTION.
TO THE LEFT SPECTRUM, THAT'S
WHAT WE CALL THE DATA SPEAKS
SPECTRUM.
SO THESE TEND TO BE COMPLETELY
DATA DRIVEN ALGORITHMS, SO THE
ADVANTAGE OF USING DATA DRIVEN
COMPLETELY DRIVEN ALGORITHM IS
THAT WE'RE NOT MAKING ANY MORE
ASSUMPTIONS, SO AS YOU MINIMIZE
THAT, THE PREDICTION CAN BE
WRONG.
BUT ON THE OTHER HAND, THE ISSUE
IS THAT BECAUSE THEY ARE SO DATA
DRIVEN, SOMETIMES THESE
ALGORITHMS WILL MAKE A
PREDICTION THAT APPEARS TO BE
CORRECT BUT IT'S DIFFICULT TO
EXPLAIN.
SO ACCURACY VERSUS INTERPRET
ABILITY ON ALL THESE METHODS.
THERE'S NO SINGLE METHOD THAT
WORKS IN HAUL CASES AND IN
DIFFERENT CONTEXTS, IN THE
CONTEXT OF CLINICAL DECISION WE
MIGHT BE WISE TO USE SOME METHOD
THAT IS MORE INTERPRETABLE,
WHEREAS IN SOME OTHER SITUATION
ACCURACY IS VERY IMPORTANT,
MAYBE DATA DRIVEN MAY BE MORE
APPROPRIATE.
SO THERE'S NO ONE SINGLE METHOD
OUT THERE THAT WILL SOLVE ALL
THE PROBLEMS.
IN MOST CASES, PEOPLE WILL TRY A
NUMBER OF DIFFERENT METHODS
ANYWAY, I THINK THAT'S PROBABLY
ONE OF THE BEST PRACTICES OF
MACHINE LEARNING IS THAT YOU
DON'T WANT TO USE ONE SINGLE
METHOD, YOU WANT TO USE A NUMBER
OF DIFFERENT METHODS.
SO THIS SLIDE IS AN OVERVIEW OF
THE DIFFERENT METHODOLOGIES, SO
I'M GOING TO REVIEW A COUPLE IN
THE NEXT FEW SLIDES.
NEXT SLIDE IS LOGISTIC
REGRESSION, THIS IS FOR
CLASSIFICATION OF A CATEGORICAL
OUTCOME.
THIS METHOD BELONGS TO THE RIGHT
SPECTRUM THAT IS A MODEL BASED
METHOD.
SO THE IDEA IS THAT WE'RE GOING
TO USE REGRESSION TO ESTIMATE
THE PROBABILITY OF A GIVEN
EVENT, GIVEN SOME VALUE.
IN THE EXAMPLE OF USING HPV
ASSAYS TO MAKE CANCER DIAGNOSIS,
SO YOU CAN USE REGRESSION -- YOU
CAN USE LOGISTIC REGRESSION,
WE'RE GOING TO ESTIMATE THE
POSSIBILITY OF A CANCER
DIAGNOSIS GIVEN THE HPV VALUES,
RIGHT?
SO THIS IS WHAT WE CAN ESTIMATE
USING LOGISTIC REGRESSION.
AND THEN THE INFERENCING OF THE
SUPERVISED LEARNING IS THAT
WE'RE GOING TO MAKE A PREDICTION
BASED ON THE DICHOTOMY.
IF THE PROBABILITY IS ABOUT 50%,
THEN IT IS A CANCER DIAGNOSIS.
OTHERWISE, IF IT'S NOT --
OTHERWISE, IT IS NOT.
SO AGAIN, THE ADVANTAGE OF
LOGISTICS REGRESSION IS THAT
BECAUSE IT'S A SPATIAL MODEL,
IT'S EASY TO INTERPRET,
INTERPRETATION IS SUPER-FAST,
AND IN MANY SITUATIONS WHEN THEY
ARE READ FEATURE INPUT WORKS
WELL.
DISADVANTAGE OF USING LOGISTIC
REGRESSION, WITH MANY FEATURES S
THAT MODEL SPECIFICATION --
MODELING INTERACTIONS CAN BE A
CHAL TOANG THE ACCURACY, SO
SOMETHING WE NEED TO THINK ABOUT
WHEN USING LOGISTIC REGRESSION.
THERE ARE MANY OTHER METHODS OUT
THERE TO TRY TO EXTEND LOGISTIC
REGRESSIONS TO REDUCE LEE -- FOR
RELIANCE ON MODEL ASSUMPTIONS,
SO THIS METHOD CAN ACTUALLY BE
USED AND THE ADVANTAGE OF USING
THESE REMEDIES IS THAT IT
DOESN'T TAKE AWAY THE
INTERPRETABILITY OF LOGISTIC
REGRESSION WHILE IMPROVING THE
ACCURACY.
SO THIS IS THE FIRST EXAMPLE OF
A SUPERVISED LEARNING METHOD
USING LOGISTIC REGRESSION.
THE SECOND TYPE OF SUPERVISED
LEARNING METHOD ALSO FOR
CLASSIFICATION IS CALLED
CLASSIFICATION TREES.
SO AS YOU CAN SEE IN THE
DIAGRAM, IT'S CREATED WHAT THE
OUTPUT LOOKS LIKE.
SO IN THE CLASSIFICATION TREE,
AGAIN, YOU TAKE ALL THE
FEATURES, IN THIS CASE IN THIS
EXAMPLE, HPV ASSAY TO PREDICT
WHETHER IT'S A CANCER DIAGNOSIS.
SO IN THE OUTPUT OF A
CLASSIFICATION TREE WOULD BE A
DECISION TREE AT THE VERY TOP.
SO FOR EXAMPLE, THE VERY FIRST
DECISION POINT, IF WE LOOK AT
CHANNEL 31, SO CHANNEL 31 IS
LARGE, MEANING THAT CHANNEL 31
IS GREAT THAN .031, THEN IT IS
GOING TO BE A CANCER DIAGNOSIS.
BUT IF CHANNEL 31 IS LESS THAN
.031, WE MOVE TO THE NEXT
DECISION POINT, AND IF YOU LOOK
AT CHANNEL 16, AND IF CHANNEL 16
IS GREATER THAN .029, THEN IT'S
ALSO A CANCER DIAGNOSIS.
OTHERWISE, WE MOVE TO THE NEXT
DECISION POINT, WHICH LOOKS AT
CHANNEL NUMBER 18.
AND AGAIN, IF CHANNEL 18 IS
GREATER THAN .03 1-RBGS IT'S A
CANCER DIAGNOSIS, OTHERWISE WE
MOVE PAST CHANNEL 3 IS.
ONE FEATURE IS THAT YOU CAN USE
THE SAME INPUT FEATURE MULTIPLE
TIMES TO MAKE DECISIONS.
AND THE DECISION ALGORITHM, IF
ALL THESE CHANNELS HAVE LOWER
VALUES THAN THIS THRESHOLD, THEN
YOU'LL BE A NEGATIVE, NONCANCER
DIAGNOSIS.
SO THE ADVANTAGE OF USING
CLASSIFICATION TREES IS THAT IS
VERY CLEAR, SO WE CAN PUT OUR
THUMB IN FRONT OF PEOPLE, PEOPLE
WILL FOLLOW THE CHART, AND THIS
IS EASY TO EXPLAIN, IT'S VERY
INTUITIVE, THAT WHEN HPV CHANNEL
HAS LOW VALUE, IT INCREASES
VALUE IN CANCER DIAGNOSIS, SO
THIS IS REALLY THE MAIN EVENT
USING CLASSIFICATION TREE.
AND THERE'S SOME TECHNIQUE THAT
WE WANT TO PREVENT, OVER FITTING
WHICH IS A CONCEPT IN SUPER
VICED LEARNING ESPECIALLY IN
THOSE METHODS THAT WE DON'T WANT
TO MAKE THE METHOD SO COMPLEX,
WHAT WE CALL OVERFITTING.
AND ONE WAY TO REMOVE THE
OVERFITTING PROBLEM WITH USING
CLASSIFICATION TREE IS THAT WE
USE A FOREST.
SO INSTEAD OF USING ONE TREE, WE
LOOK AT MANY, MANY TREES.
SO FOREST BY DEFINITION ARE AN
ENSEMBLE OF TREES.
SO RANDOM FOREST IS ACTUALLY ONE
OF THOSE ENSEMBLE LEARNING
METHODS I MENTIONED EARLIER IS
THAT THE IDEA IS THAT ON THE
SAME TRAINING DATASET, WE RUN
MANY, MANY TREES AND THEN USE
THE DIFFERENT TREES TO AVERAGE
THEM OUT AND THEN MAKE A
PREDICTION.
THERE ARE SOME EMPIRICAL STUDIES
SUGGESTING THAT RANDOM FOREST
DOES IMPROVE THE ACCURACY OF
PREDICTION, CLASSIFICATION
TREES, SIMPLY BECAUSE YOU REMOVE
THE OVERFITTING PROBLEM AND
REDUCE ERROR IN PREDICTION.
BUT DISADVANTAGE OF USING FOREST
IS THAT IT TAKES AWAY THE
INTERPRETABILITY OF THE TREE,
BECAUSE FOR CLASSIFICATION WE
CAN PRESENT EVERYONE WITH A TREE
LIKE THAT SO THAT THE
DECISION-MAKING PROCESS IS VERY
CLEAR, BUT FOR FOREST IT BECOMES
A BLACK BOX.
SO THIS IS THE SECOND METHOD.
ANOTHER METHOD THAT YOU MAY HAVE
HEARD OF WHICH ACTUALLY IS NOT A
GOOD APPLICATION IN THIS CASE,
BUT I RUN THIS ANYWAY IS A
NEURAL NETWORK.
THE IDEA OF A NEURAL NETWORK IS
THAT YOU TRY TO MODEL THE DATA
AFTER THE WAY THAT WE OBSERVE
HOW LIKE NEURON COMMUNICATE WITH
EACH OTHER IN THE SYNERGISTIC
SYSTEM.
I FIGURED I WOULD TALK ABOUT
THIS SINCE THIS IS A
PRESENTATION IN NINDS ABOUT THE
NEURAL NETWORK, BUT THE IDEA IS
THAT JUST STOPS THERE, SO THIS
CAN BE VIEWED AS A NEURON AND
THEN THE IDEA IS THAT YOU
PROPAGATE, YOU TAKE AN INPUT AND
THEN TRANSFER THE INPUT INTO
SOME NUMBER AND THEN COMMUNICATE
TO THE NEXT LAYER OF NEURONS AND
THEN KEEP PROPAGATING AND THEN
TO MAKE A PREDICTION TO OUTCOME.
SO EACH PROPAGATION IS BASED ON
SWHOOM WE CALL TRANSFER -- BASED
ON WHAT WE CALL TRANSFER
FUNCTION, AND HOW DO WE
DETERMINE THE TRANSFER FUNCTION
IS SOMETHING WE NEED TO SPECIFY,
BUT THE IDEA IS THAT WE'RE GOING
TO USE THE DATA TO DETERMINE THE
SPECIFICS OF THIS TRANSFER
FUNCTION IN ORDER TO MAKE A
PREDICTION.
SO THIS IS AN EXAMPLE WHERE
THERE'S ONE HIDDEN LAYER.
SO THIS HIDDEN LAYER INDICATES
THE FEATURE, SO FOR SIMPLICITY
SAKE I ONLY USED SIX CHANNELS,
SIX FEATURES AS INPUT.
SO ONE LAYER AND THEN WE HAVE
ONE OUTPUT AS A PREDICTION.
THE COMPLEXITY OF THE NEURAL
NETWORK CAN INCREASE
INDEFINITELY BY ADDING NEW
LAYERS.
SO IN THIS EXAMPLE, AGAIN, WE
HAVE THE SIX INPUT FEATURES BUT
WE HAVE TWO LAYERS.
SO AS WE IMPROVE -- AS WE
INCREASE THE NUMBER OF LAYERS,
WE INCREASE THE COMPLEXITY OF
THE NETWORK, THE IDEA IS THAT
YOU'LL EVENTUALLY GET A MUCH
BETTER ACCURACY IF WE HAVE
SUFFICIENT COMPLEXITY TO MODEL.
BUT AGAIN, IT HAS THE ISSUE OF
OVERFITTING IF WE ADD TOO MANY
LAYERS TO IT.
BUT IN TERMS OF ACCURACY, I
THINK NEURAL NETWORK HAS GOT A
LOT OF ATTENTION BECAUSE IT'S
DOING VERY WELL, ESPECIALLY IN
THE APPLICATION OF IMAGING AND
RECOGNITION OF IMAGES.
SO SEVERAL YEARS AGO, I THINK
IBM HAD THIS NEURAL NETWORK TO
REALLY TO TRAIN THE ALGORITHM TO
RECOGNIZE PICTURES, SO THAT WAS
MANY YEARS AGO, IT'S BECOME MUCH
EASIER NOW THESE DAYS.
BUT THAT WAS BASED ON NEURAL
NETWORK.
THERE ARE A NUMBER OF POTENTIAL
ISSUES OF NEURAL NETWORK
ESPECIALLY IN THE CONTEXT OF
CLINICAL APPLICATION.
FIRST, PICKING A NUMBER, WE HAVE
A LOT OF DATA, IT DEPENDS ON HOW
WE SPECIFY THE STARTING VALUES.
THE JOWLT PUT OF A NEURAL
NETWORK CAN BE RANDOM, SO
IT'S -- THE OUTPUT OF A NEURAL
NETWORK CAN BE RANDOM, SO WE MAY
NOT HAVE THAT AMOUNT OF DATA TO
WARRANT USING A NEURAL NETWORK.
AND IT CAN CRASH IF THE DATA IS
TOO SMALL.
THE MAIN DATA IN THE APPLICATION
IS THAT IT'S A COMPLETE BLACK
BOX.
NOT ONLY THAT WE CANNOT
INTERPRET HOW DID THE NETWORK
MAKE THE PREDICTION.
AND ALSO THAT WE DO NOT KNOW
WHETHER THAT PREDICTION -- WHY
THAT PREDICTION WILL WORK.
SO CLINICALLY IT WORKS VERY
WELL, BUT THERE'S NO THEORY TO
SUGGEST WHY NEURAL NETWORK
WORKS.
SO THAT'S THE PART THIS WE NEED
TO BE THINKING ABOUT WHEN WE
WANT TO USE THIS METHOD IN
CLINICAL APPLICATION.
SO WHICH LEADS TO THE NEXT PART
OF THE PRESENTATION IS ABOUT HOW
DO WE EVALUATE AN ALGORITHM.
I MENTIONED THE CONCEPT OF
OVERFITTING, SO FOR ANY METHODS,
IF YOU MAKE IT COMPLEX ENOUGH IN
THE CONTEXT OF NEURAL NETWORK,
IF YOU ADD ENOUGH NUMBER OF
LAYERS IN CLASSIFICATION TREES
AND RANDOM FORESTS, IF YOU ADD
ENOUGH TREES, YOU CAN ALWAYS
IMPROVE THE ACCURACY IN THE
TRAINING DATASET, BUT THAT'S NOT
THE GOAL OF SUPERVISED LEARNING.
THE GOAL OF LEARNING, SUPERVISED
LEARNING OR PREDICTION IS THE
PREDICTION OF WHAT WILL HAPPEN
IN FUTURE INSTANCES, NOT IN THE
TRAINING DATA.
SO THE IDEA IS THAT WHAT MAY
WORK WELL IF WE WANT TO USE THE
TRAINING TO VALIDATE IT.
SO ONE IDEA TO EVALUATE AND
TRAIN THE ALGORITHM IS THAT WE
WANT YOU TO USE ADDITIONAL DATA
WHETHER THE ALGORITHM WILL WORK.
SO CROSS VALIDATION, SO THE
CONCEPT OF CROSS VALIDATION IS
THAT INSTEAD USING THE ENTIRE
DATASET TO TRAIN THE ALGORITHM,
WE SET ASIDE, FOR EXAMPLE, 20%
DATA NOT TO BE USING THE
TRAINING, WE JUST USE THE DATA
FOR TRAINING AND THEN BASED ON
THE ALGORITHM WE USE 20% OF THE
DATA TO EVALUATE THE ACCURACY OF
THE ALGORITHM.
SO IN THE IDEAL WORLD, WHAT WE
CAN DO IS THAT AFTER WE'RE
TRAINING AN ALGORITHM, WE
COLLECT DATA FROM A NEW COHORT
AND THEN USE DATA FROM A NEW
COHORT TO VALIDATE ACCURACY.
SO IT'S IMPORTANT NOT TO ONLY
LOOK AT ACCURACY OF THE
ALGORITHM WITHIN THE TRAINING
DATASET, SO THE FIGURE HERE
INDICATES THAT WE'RE USING TWO
LEARNING OUTCOMES IN THIS
EXAMPLE, AND THE ACCURACY
METRICS IS CALLED AREA UNDER THE
CURVE, WHICH IS A SUMMARY OF
SPECIFICITY.
IF YOU LOOK, AUC, HIGH IS GOOD,
ONE PERFECT DIAGNOSTIC. SO IF
YOU LOOK AT AUC JUST WITHIN THE
TRAINING DATA, THEY BOTH MAP ALL
THE METHODS IN DIFFERENT
POPULATION, ACHIEVE OVER 90% YOU
SEE.
IF YOU USE CROSS VALIDATION, WE
FIND THAT THE CROSS VALIDATION
USES ONLY ABOUT 80%, WHICH IS
ACTUALLY VERY HIGH, BUT IT'S NOT
ABOVE 90%.
SO WHEN WE DO -- WHEN WE APPLY
SUPERVISED LEARNING, IT IS
IMPORTANT FOR US TO LOOK AT THE
CROSS VALIDATION ERROR INSTEAD
OF THE TRAINING ERROR AS A WAY
TO READ THE ALGORITHMS.
LET ME MOVE QUICKLY TO THE AREA
OF UNSUPERVISED LEARNING.
TWO INTERESTING QUESTIONS ABOUT
UNSUPERVISED LEARNING.
ONE IS ON IDENTIFYING HOW MUCH
IS THE POPULATION, THE OTHER IS
DIMENSION REDUCTION.
SO JUST A QUICK EXAMPLE.
WHAT YOU'RE SEEING IN THIS HEAT
MAP IS THAT EACH CORE RESPONDS
TO ONE CANCER TUMOR AND EACH
COLUMN IS A GENE, SO THE COLOR
INDICATES THE EXPRESSION LEVEL
OF ON THAT GENE.
FIRST APPLICATION OF CLUSTER
ANALYSIS IN UNSUPERVISED
LEARNING IS CLUSTERING OF THE
SAMPLING UNIT N THIS EXAMPLE WE
TRIED TO CLUSTER THE CANCER
TUMORS BY LOOKING AT WHERE THE
TUMORS ARE SIMILAR TO EACH
OTHER.
SO WE TRIED TO IDENTIFY MAYBE A
HOMOGENOUS GROUP OF THESE CANCER
TUMORS, SO THAT WILL BE
CLUSTERING OF THE CANCER TUMOR,
LOOKING FOR ROW THAT IS ARE
SIMILAR.
THAT'S ONE APPLICATION FOR
INFERENCING OF UNSUPERVISED
LEARNING.
THE SECOND INTERESTING GOAL OF
UNSUPERVISED LEARNING IS THAT WE
WANT TO LOOK AT WHETHER THERE
ARE GENES THAT ARE CLUSTERED
WITH EACH OTHER, IN ORDER TO
REDUCE THE NUMBER OF GENES,
BECAUSE WE WANT TO MAKE INFLENS
OF OVER 6,000 GENES, THIS IS --
WE WANT TO MAKE SENSE OF OVER
6,06,000 GENES, THIS IS A LOT OF
GENES, WE WANTED TO IDENTIFY
GENES THAT TBLONG THE SAME
CLUSTER AND MAKE HELP STUDY IT
DOWN THE ROAD, SO ANOTHER
EXAMPLE OF UNSUPERVISED LEARNING
IS TO PERFORM CLUSTER ANALYSIS
OF THE GENES IN ORDER TO
ACCOMPLISH DIMENSION REDUCTION.
SO THESE ARE THE TWO MAIN GOALS.
AND THESE ARE THE METHODS OF
UNSUPERVISED LEARNING.
I'M GOING TO GO OVER THEM VERY,
VERY QUICKLY.
BUT THE MAIN METHOD THAT I'LL
TALK ABOUT IS K-MEAN CLUSTERING,
HIERARCHICAL CLUSTERING AND
MODEL BASED CLUSTERING.
SO THIS IS AN EXAMPLE WHERE WE
HAVE DATA OF OVER 20,000 DATA
AND THEN THE GOAL IS TO IDENTIFY
SUBPOPULATIONS OF THE PATTERN OF
THESE INDIVIDUALS WHO TAKE --
AND PUT THEM IN THE IDENTIFY OF
PATTERN BAIMPS.
THE IDEA OF CLUSTERING IS THAT
WE IDENTIFY THE NUMBER OF
CLUSTER AND THEN WE PUT EACH
GROUP TO A CLUSTER, IF THAT DATA
POINT IS COEFFICIENT OF THE
STANDARD OF THAT CLUSTER, THAT'S
WHAT WE CALL K-MEANS CLUSTERING,
SO THESE ARE THE DIFFERENT
METHODS, SO THIS IS A DIFFERENT
WAY THAT WE USE THE DATA
SOPHOMORE K-MEANS CLUSTER.
YOU CAN SEE THE RESULTS ARE
HIGHLY DIFFERENT.
SO THEY INDICATE ONE OF THE
ISSUES WITH USING K-MEANS
CLUSTERING IS THAT FIRST OF ALL,
THE SCALING IS CRITICAL.
SO IN THIS EXAMPLE, ONLY TWO
INPUT FEATURES TO MAKE IT
SIMPLE.
SO I REDUCED THE -- USED THE
NUMBER OF STEPS AS INPUT
FEATURES AND THE OTHER IS CALLED
ACTIVITY INPUT -- IS MIDDAY
ACTIVITY AS ONE OF THE FEATURES.
YOU SEE WE GET THESE TWO
CLUSTERS, BUT THAT IS BASICALLY
DRIVEN BY THE NUMBER OF STEPS.
THE REASON IS THAT NUMBER OF
STEPS HAS A MUCH BIGGER VARIANCE
SO THAT IT DRIVES BASICALLY
THAT'S THE DOMINANT POINT, SO WE
SCALE THE INPUTTED SO THAT --
THE INPUT SO THAT THERE'S NO ONE
SINGLE VARIABLE THAT WILL
DOMINATE, AND AFTER
STANDARDIZATION SHES WE GET VERR
STANDARDIZATION WE GET VERY
DIFFERENT ANSWERS.
SO IN GENERAL, SCALING IS VERY
BENEFICIAL FOR MACHINE LEARNING
AND PARTICULARLY FOR K-MEANS
CLUSTERING.
THE SEKSD EXAMPLE IS
HIERARCHICAL CLUSTERING.
AGAIN, SCANNING IS VERY
IMPORTANT, SO IN THIS CASE WE
USED THE RIGHT SCALE TO DO IT,
AND IN HIERARCHICAL CLUSTERING,
YOU PROFILE, HOW WHAT IS CALLED
A DENDROGRAM, HOW CLOSE DATA
POINT TO THE OTHER, IT IS VERY
INTUITIVE AND LTS VERY CLEAR HOW
LIKE TWO POINTS WILL BECOME THE
SAME CLUSTER.
THE TWO MAIN ISSUES, ONE AGAIN
IS THAT COMPUTATION, IT IS VERY,
VERY CHALLENGING.
THIS IS A SMALL PROBLEM, IT TOOK
ME A FEW MINUTES.
FOR LARGER PROBLEMS, YOU WOULD
NEED A LOT OF COMPUTER POWER.
THE OTHER ISSUE IS THAT
SOMETIMES YOU CREATE SMALL
CLUSTERS, SO WE SEE THAT THIS IS
BIG, 20,000 DATA POINTS BUT WE
ONLY HAVE A HANDFUL BELONGING TO
ONE CLUSTER.
SO SOMETIMES WE MAY BE SEEING A
LOT OF THESE SMALL CLUSTER
PROBLEMS WITH HIERARCHICAL
CLUSTERING METHOD.
THE LAST APPROACH IS ON MODEL
BASED CLUSTERING METHOD, AND IT
IS ALSO VERY COMPUTATIONALLY
INTENSIVE.
THE ADVANTAGE OF USING MODEL
BASED IS THAT IT IS A PRINCIPAL
WAY THAT WE MAKE MODEL
ASSUMPTION TO STRUCTURE DATA SO
THAT SCALING IS PART OF THE
MODEL.
SO THAT PART IS BEING TAKEN CARE
OF, AND WE ARE APPLYING THIS TO
THE SAME DATASET, THE DATA, TO
USE MODEL BASED CLUSTERING TO
IDENTIFY PATTERNS.
SO IN THIS EXAMPLE, SO THE
METHOD IDENTIFIED THREE
DIFFERENT CLUSTERS, AND EACH
CURVE IN EACH CLUSTER INDICATES
THE AVERAGE ACTIVITY DURING,
OVER DURING THE DAY, FROM
MIDNIGHT TO MIDNIGHT.
SO BASICALLY THIS IS UNSUPER
VICED LEARNING, SO ALL WE GAVE
TO THE ALGORITHM IS THE FIPPIT
DATA WITHOUT ANY SUPERVISED
OUTCOME AND THEN WE JUST COME UP
WITH EIGHT DIFFERENT PATTERNS,
AND THEN WE TRY TO ASSOCIATE
THESE PATTERNS, AND IN
PARTICULAR, WE IDENTIFY THAT
CLUSTER 5 IS KIND OF INTERESTING
WITH WEEKDAYS.
SO THIS IS A HEAT MAP
REPRESENTING THE PROPORTION OF
PARTICIPANTS IN EACH PATTERN ON
A GIVEN DAY.
SO ON THE WEEKDAY, WE SEE THAT
THERE'S A STRONG SIGNAL THAT
MOST PEOPLE ARE ENGAGING IN
CLUSTER 5 ACTIVITY.
WHAT HAPPENS IS THAT IF YOU PAY
ATTENTION TO THIS CLUSTER 5,
THERE'S PEAK ACTIVITIES
OCCURRING AT 8:00, NOON, 5:00,
BASICALLY TWO COMMUTER TIMES AND
LUNCH HOUR.
SO THIS IS ACTUALLY WHAT THE
CLUSTER ANALYSIS CAN HELP YOU
IDENTIFY A PATTERN THAT WE ARE
NOT EVEN AWARE OF THAT MAY BE
EXISTING.
SO THIS IS ACTUALLY ONE USEFUL
WAY TO USE CLUSTER ANALYSIS.
OUR FINAL SLIDE, JUST TO
SUMMARIZE WHAT WE TOUCHED ON,
MACHINE LEARNING IS A VAST AREA
AND IT'S VERY FAST GROWING, BUT
HOPEFULLY I'LL WALK THROUGH THE
MAIN ONE, THE MAIN APPLICATION,
WHICH IS SUPERVISED AND
UNSUPERVISED LEARNING AND I WANT
TO EMPHASIZE THAT THIS IS A
LEARNING METHOD COMPARED TO
HYPOTHESIS TESTING.
SO WE DO NOT HAVE -- TO PLACE IT
IN THE RIGHT CONTEXT, WE
PROBABLY WANT TO THINK ABOUT
THIS AS DISCOVERY, HYPOTHESIS
GENERATING, SO THAT ONCE WE'RE
GENERATING A HYPOTHESIS, THIS IS
SOMETHING THAT NEEDS TO BE
TESTED DOWN THE ROAD IN A MORE
RIGOROUS FASHION.
AND THEN I'LL TOUCH ON A NUMBER
OF ISSUES IN TERMS OF USING EACH
METHOD, BUT INCLUDING THE
IMPORTANCE OF THE SCALE, THE
FEATURE INPUTS, STARTING VALUES
OF SOME ALGORITHMS.
IMPORTANT APPLICATION OF MACHINE
LEARNING IS THAT WE NEED TO
REPRESENT DATA IN A PROPER WAY.
SO THE SUCCESS OF AN ALGORITHM
REALLY DEPENDS ON WHETHER DATA
CAN BE REPRESENTED IN A PROPER
WAY.
SO THAT'S A CONCEPT, AND
EMBEDDING AND TOKENNIZATION.
THIS IS SOMETHING I HAVE NOT
QUITE TOUCHED UPON, BUT IN THE
PREVIOUS EXAMPLE, SO THE IDEA
WHY THIS WORKS SO WELL IS THAT
WE REPRESENT THE TYPE OF DATA
USING WHAT WE CALL QCA
REPRESENTATION TO REDUCE
DIMENSION.
SO HELPING -- SO HAVING THE
RIGHT DATA REPRESENTATION IS
VERY CRITICAL, AND IT IS VERY
DATASET SPECIFIC.
SO FOR EACH LEARNING ALGORITHM,
TWO PART, ONE IS TRAINING, THE
OTHER IS INFERENCING.
SO I THINK I FOCUSED MUCH MORE
ON THE TRAINING COMPONENT
ELEMENT IN THIS PROCESS, BUT THE
INFERENCE IS GETTING MORE AND
MORE IMPORTANT, ESPECIALLY NOW
WE'RE TRYING TO USE MACHINE
LEARNING FOR STUFF THAN JUST
PREDICTING A NUMBER.
BUT WITH THAT, I THINK THIS IS
MY LAST SLIDE, AND THANK YOU FOR
STAYING WITH ME.
>> ANY QUESTIONS FROM ANYBODY?
[PAUSE]
>> I DON'T SEE ANYTHING IN CHAT.
>> I SEE ONE QUESTION.
IS LLM ALSO ML?
LLM, LARGE LANGUAGE MODEL,
THAT'S THE BACKBONE OF
GENERATIVE AI THESE DAYS.
A LOT OF TRAINING USE THE
CONCEPT FOR EXAMPLE, LLM IS
NOTHING BUT WHERE THE DATA
SOURCE IS AND THEN HOW THEY DO
THE GENERATIVE AI IS THAT FOR
THE TRAINING PART I THINK A LOT
OF THEM USE NEURAL NETWORK FOR
TRAINING.
BUT BEFORE THEY GET TO THE
NEURAL NETWORK, THEY COULD NEED
TO DO A LOT OF EMBEDDING AND A
LOT OF ORG.
THAT'S THE PART I MENTIONED
TOWARDS THE END OF THE
PRESENTATION.
BUT ONE IS DIFFERENT FROM
GENERATIVE AI AND WHAT I TALK
ABOUT IS THAT IN THE TRADITIONAL
MACHINE LEARNING, WE WANT TO DO
A PREDICTION, RIGHT?
SO FOR EXAMPLE, WITH THESE
CLINICAL FEATURES FROM GENERAL
MEDICAL RECORDS, WE WANT TO
DEFENS --WE WANT TO GIVE A RISKF
PATIENTS, THAT'S A PREDICTION.
IN THE CONTEXT OF GENERATIVE AI
INFERENCING IS MUCH HARDER
BECAUSE IT'S NOW NOT ASKING US
TO PRODUCE NUMBER, IT'S ASKING
US TO PRODUCE AN IMAGE.
WE WEREN'T TRYING TO RECOGNIZE
THE IMAGE.
WE WANT TO SAY PRODUCE A CAT,
RIGHT?
SO RECOGNIZING IT'S A CAT.
SO THE INFERENCING PART BECOMES
MUCH MORE COMPLICATED AND THIS
IS THE PART THAT TRADITIONALLY
WE HAVE NOT BEEN FOCUSING ON,
AND STILL MAY NOT BE SOMETHING
THAT WE WANT TO THINK TOO MUCH
ABOUT IN CLINICAL RESOURCE, BUT
THAT'S AN INTERESTING QUESTION.
I HOPE IT HELPS.
>> IT DOES.
THANK YOU.
[PAUSE]
>> I'M SURPRISED WE ONLY HAD ONE
QUESTION ABOUT GENERATIVE AI, I
THOUGHT PEOPLE WOULD BE ASKING
THAT QUESTION.
BUT ANYHOW, THANKS FOR THE
QUESTION.
[PAUSE]
>> THANK YOU, KEN.
THANK YOU, EVERYONE, FOR JOINING
TODAY, AND FOR JOINING OUR
SERIES.
>> THANK YOU VERY MUCH.