Blended Course Offering by US
Faculty in Vietnam: Potential Model
for India
Prof. Rao Vemuri
University of California, Davis
29 April 2015
INTRO TO VIETNAM
Vietnam
• Like India, Vietnamese had to fight several
wars with invading armies and colonial powers
• They fought the French and defeated them at
Dien Bien Phu
• Then they (N. Vietnam) fought the US and
defeated them
• Now US is helping them to rebuild
• My visit to Vietnam is a part of this process!
Demographics
• Vietnam means “southern country”
(dakshiNApathaM)
• Tibeto-Burman, Polynesian and Chinese stock
• Most do not claim a religion. Buddhists and
Christians are a minority (8% each)
• Most are young, post-war generation
• Most are small in stature and lean in build
• They eat very little sweets
• Only Asian country to adapt Roman script
HOW IT HAPPENED?
How it Got Started?
• Vietnam Educational Foundation (funded by
the US State Department) hired six instructors
to teach classes to students in Vietnam
• The courses must be from the host college
curriculum
• The classes should be taught at the regularly
scheduled class-time for semester/ full year
• Each class session 90 minutes duration
Preparation
• VEF invited us to Washington DC and briefed
us about Vietnam, what to expect, how to
handle ourselves
• Each of us presented our plans:
– How we intend to conduct the class
– What notes/books we are going to use
– What tools/technologies we intend to use
– Whether we intend to combine local students and
remote students in the class sessions
Guidelines
• Instructor visits Vietnam before the semester
begins
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Gets to know the campus
Meets the administrators
Inspects the classroom and facilities
Teams up with a local instructor
Both the US Instructor and Vietnamese instructor
meet the students in the classroom setting
– US Instructor spends one week in Vietnam and returns
back and delivers the lectures over the Internet
• I chose to stay in Vietnam for three weeks and delivered the
first few lectures in person
Class Preparation
• I prepared written class notes as PDF files and
posted them on a class web site at the start of
the semester. No additional text book.
• I prepared the class lectures as PPT slides and
posted them on the class web site the day
before the lecture
• I purchased several copies of classical books
and gifted them to the department
• I gave away several of my own books as a gift
Webinar Presentation
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Most of the lectures were webinars
Listening to webinar for 90 minutes is hard
I divided the webinar into 30-minute modules
After each module, I take a 5-min. break
Students can use that time to
– Ask questions, Talk among themselves, Talk to the
local instructor
• I interspersed my lecture with multiple-choice
questions to keep the students engaged
Monitoring
• My Vietnamese counterpart (a junior lecturer
with a Masters degree) sat in the class room
with her own computer connected to my
standby computer via Skype
• The webinar was delivered via GoToMeeting
and continually monitored by IUCEE personnel
from Hyderabad
• The local instructor monitored the class,
communicated problems and questions
HW/Examinations
• I assigned regular homework (paper & pencil and
programming)
• I gave the solutions to the local instructor
• The local instructor graded them
• One mid-term & a final examination
• One term project involving programming
• Students make a presentation of their results
• I graded the examinations and assigned the final
class grade
What Did I Learn?
• It can be done
• It requires commitment from two sides
• It requires adaptation from two sides
Other Benefits
• A Vietnamese delegation from the University
of Science visited our campus to explore
further collaboration
• The instructor who helped me in Vietnam is
now doing Ph. D at the U of Denver
• One of the professors is now collaborating
with me to establish a Information Security
Lab with Cryptography, Machine Learning
Deductive Approach
• Top-down
– From general principles to a case study
– Given Newton’s laws apply them to various cases
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Pendulum
Inclined plane
Falling bodies
Planetary motions
Inductive Approach
• Bottoms up
– From a case study to generalization
– Given a specific context and experience, find out
what works and derive general principles
– I taught two courses to students in Vietnam over a
period of one year
– I want to share that experience and see if we can
learn some general principles and apply them to
the Indian context
Definition
• A Blended course involves instructor and
learners working together
– in mixed delivery modes
• Face-to-face & Over-the-Internet
• Technology mediated
• to accomplish learning outcomes that are pedagogically
supported through assignments, activities, and
assessments as appropriate for a given mode and which
bridge course environments in a manner meaningful to
the learner.
What is Blended Learning
• hybrid/blended learning represents a
convergence of educational theory and
technology
• Three types of blends:
– Students meet on campus and participate in
asynchronous on-line learning activities
– Synchronous meetings/social network technologies
blended with asynchronous work with face-to-face
meetings to structure a course
– Combination of campus-based plus on-line students
Introduction to Machine
Learning: Syllabus
Rao Vemuri
rvemuri@gmail.com
Fall 2013
Pre-requisites
• Calculus
– Chain rule of differentiation. Finding maxima and
minima of functions, logarithms, exponentials
• Probability
– Sample spaces. Axioms of probability,
conditional probability, Bayes formula
– Normal distribution
• MATLAB or equivalent
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Machine Learning:
Job Opportunities
• Your chance of getting a good job will increase
– if you have a knowledge of MATLAB or R
– If you have skills in handling Big Data
– If you can make sense out of huge data bases
• Know something about (not in your syllabus)
– Big Data Analytics, MapReduce, Hadoop, etc
– Handling “Big Data” is not part of this syllabus
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Syllabus
• Topic 1. POSING MACHINE LEARNING PROBLEM
– What is machine learning?
– How do you set up the problem?
• Topic 2: DECISION TREE LEARNING
– Decision tree representation, What types of problems
are suitable for decision trees? Inductive bias in
decision trees, Examples
– You will write a Matlab program to generate a simple
decision tree
– You can do a class project using this method
Syllabus
• Topic 3. ARTIFICIAL NEURAL NETWORKS
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What are neural nets? Problems for NN
Perceptrons, Training a Perceptron
Gradient Descent and Delta rule
Multi-layer networks and Back Propagation
• Topic 4. EVALUATING A HYPOTHESIS
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Estimating the accuracy of a hypothesis
Basics of sampling theory
Bias and variance
The Central Limit Theorem
Hypothesis testing.
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Syllabus
• Topic 5. BAYESIAN LEARNING
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Bayes’ Theorem
Maximum Likelihood and Least Squares Hypotheses
Naïve Bayes’ classifier
Learning to Classify Web Documents
• Topic 6. GENETIC ALGORITHMS
– Genetic operators, Fitness functions and Selection,
mutation, Examples
– Schema Theorem
– Genetic Programming
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Text Books
• Tom Mitchell, Machine Learning, McGraw Hill,
Second Edition
• Tom Mitchell’s web site:
http://www.cs.cmu.edu/~tom/10701_sp11/le
ctures.shtml
• Any good book on MATLAB
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Data Sources
• https://www.kaggle.com/competitions
• http://archive.ics.uci.edu/ml/machinelearning- databases/spambase/
Advanced Topics in
Machine Learning
Rao Vemuri
UC Davis
rvemuri@gmail.com
Pre-requisites
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Introduction to Machine Learning
Introduction to Probability and Statistics
Ability to use MATLAB or another language
This is an graduate level course and the
students are expected to have some
sophistication with mathematics
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Syllabus1: Advanced ML
• Supervised Learning
– Notation, LMS, Gradient methods
– Log-likelihood
– Logistic Regression
• Generalized Linear Models (GLM)
• Generative Learning Algorithms (GLA)
– Gaussian discriminant analysis, Naïve Bayes
• Perceptron, SVM
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Syllabus2: Advanced ML
• Learning Theory/Regularization/Model Selection
• Unsupervised Learning
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Clustering
Expectation Maximization (EM)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
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Data Sets for Your Use
• You should be familiar with data sets for use in
home works and projects.
– UC Irvine has a Machine Learning data Archive
– Learn where to find data archives for finger print
data, hand-written character data, photographs of
faces, protein data, genetics data, and so on.
– Learn how to download the files into your Matlab
folder and learn how to “pre-process” that data
using Matlab commands
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Learn to Create Your Own Data
• Generate your own data on a computer
– Take a sinusoid, sample it, corrupt it with 5% noise
– Take a polynomial, sample it, corrupt it with noise.
• Data can be created by building a model
– Build a model of the Earth and generate
seismograms by exploding a charge at different
depths on the model (this is a major effort in
itself!)
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Some Interesting Data Sets
• Brain Imaging (fmIR) data available here:
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/th
eo-81/www/
• Image Segmentation data
http://www.eecs.berkeley.edu/Research/Projects/C
S/vision/grouping/segbench/
• News Group text data
http://www.cs.cmu.edu/afs/cs/project/theo11/www/naive-bayes.html
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Other Data & Code Sources
• Character Recognition data
http://ai.stanford.edu/~btaskar/ocr/
• UCI Data Archive
http://archive.ics.uci.edu/ml/machine-learningdatabases/spambase/
• Kaggle Competitions
https://www.kaggle.com/competitions
• Matlab Code by Mark Schmidt (2005-2013)
http://www.di.ens.fr/~mschmidt/Software/code.ht
ml
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References
There are a number of useful reference books. Each covers material
that is more advanced than the class material.
• Bishop, Christopher. Neural Networks for Pattern Recognition. New
York, NY: Oxford University Press, 1995. ISBN: 9780198538646.
• Duda, Richard, Peter Hart, and David Stork. Pattern Classification.
2nd ed. New York, NY: Wiley-Interscience, 2000. ISBN:
9780471056690.
• Hastie, T., R. Tibshirani, and J. H. Friedman. The Elements of
Statistical Learning: Data Mining, Inference and Prediction. New
York, NY: Springer, 2001. ISBN: 9780387952840.
• MacKay, David. Information Theory, Inference, and Learning
Algorithms. Cambridge, UK: Cambridge University Press, 2003.
ISBN: 9780521642989.
Free On-line Books - 1
• David C. MacKay, Information Theory,
Inference and Learning, Cambridge University
Press, 2003. Freely available on-line at
http://www.inference.phy.cam.ac.uk/mackay/itila/
A very well-written book with excellent
emphasis on many machine learning topics
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Free On-line Books - 2
• David Barber, Bayesian Reasoning and
Machine Learning, Cambridge University
Press, 2012. Freely available on-line at
http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki.php?n
=Brm1.online
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Free On-line Books - 3
• Trevor Hastie, Robert Tibshirani and Jerome
Friedman, The Elements of Statistical
Learning, Springer 2009, Freely available online at
http://www-stat.stanford.edu/~tibs/ElemStatLearn
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Free On-line Videos
• Embedding Uncertainty
http://scpro.streamuk.com/uk/player/Default.aspx?wid
=7739
• Probabilistic Graphical Models
http://videolectures.net/mlss06tw_roweis_mlpgm/
• Introduction to Machine Learning
http://videolectures.net/bootcamp2010_murray_iml/
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