Blended Course Offering by US Faculty in Vietnam: Potential Model for India

Prof. Rao VemuriUniversity 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– 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

• 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• 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 Vemurirvemuri@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

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– 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– 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– 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/lectures.shtml

• Any good book on MATLAB

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Data Sources

• https://www.kaggle.com/competitions• http://archive.ics.uci.edu/ml/machine-

learning- databases/spambase/

Advanced Topics in Machine Learning

Rao VemuriUC Davis

rvemuri@gmail.com

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Pre-requisites

• 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– 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/theo-81/www/• Image Segmentation datahttp://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/• News Group text datahttp://www.cs.cmu.edu/afs/cs/project/theo-11/www/naive-bayes.html

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Other Data & Code Sources

• Character Recognition datahttp://ai.stanford.edu/~btaskar/ocr/• UCI Data Archivehttp://archive.ics.uci.edu/ml/machine-learning- databases/spambase/• Kaggle Competitionshttps://www.kaggle.com/competitions• Matlab Code by Mark Schmidt (2005-2013)http://www.di.ens.fr/~mschmidt/Software/code.html

ReferencesThere 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.

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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 on-line at

http://www-stat.stanford.edu/~tibs/ElemStatLearn

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Free On-line Videos

• Embedding Uncertaintyhttp://scpro.streamuk.com/uk/player/Default.aspx?wid=7739• Probabilistic Graphical Modelshttp://videolectures.net/mlss06tw_roweis_mlpgm/• Introduction to Machine Learninghttp://videolectures.net/bootcamp2010_murray_iml/