Welcome Talk (MLD Open House)

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Charalampos (Babis) E. Tsourakakis 3rd Year Graduate Student

Open House February 26th , 2010


  Carnegie Mellon University is a historical

university with living history.

Herbert Simon

Alan Frieze

Avrim Blum Manuel Blum Tom Mitchell, Head of MLD Carlos Guestrin John Lafferty

Alen Newell

Stephen Fienberg Larry Wasserman

Admission highly competitive!


  International Student (Hellene, ECE NTUA)   2007-­‐2009 worked with Christos Faloutsos on

Data Mining (Tensors, Graphs and Large Scale Data Mining using Hadoop)   Switched at the end of the second year.   Now, Algorithm Design (TCS) Manifold Learning (ML) Breast Cancer Evolution (App) Gary Miller e.g., Miller Primality Test

Russell Schwartz e.g., decoding human genome


Change/add advisor Immigration course: (if you want) Data analysis faculty gives talks, project, you choose advisor speaking skills ( get MS) Start doing research! (publish 1st paper)

Finish!

Propose

Summer internships

time

0

1

2

3

Coursework: 5 core courses: •  Intermediate statistics, ML •  Stat ML, Algorithms, Data mining +3 electives (1 from statistics)

TA for 2 classes

4

5 … 

Conduct research Enjoy dept. tea gathering and TGs


  Very critical choice for your career.   Great faculty to choose from.   Make a good choice by:

  choosing a project that excites you   making sure that you and your advisor have the

same research “mentality” (Ask yourself, do you like more applications, theory, a mixture of them and what proportion from each)   reading papers of your potential advisors


Year 1:       

Intermediate statistics Intro machine learning Stat. machine learning Algorithms

Year 2:

  Data mining   Advanced Discrete Math:

Additive Number Theory   Advanced Discrete Math: Mixing times and Markov Chains   Computational Methods for Biological Modeling and Simulation (Some of my electives)

Many ML classes                       

Graduate ML Statistical ML Graphical models Convex optimization Graduate AI Learning theory Bayesian methods Comp bio + learning Computational Complexity Randomized algorithms Approximation algorithms Lots of area-­‐specific machine learning (text, bio, brain, …)



  MLD Seminars:   MLD/Google seminar   Intelligence seminar   Machine learning lunch ▪  organized by students

  Many other seminars:   Theory, LTI, Robotics,…

  MLD weekly tea gathering   TGs (Thank Goodness It’s Friday)


  A city which has two main advantages which

are typically inversely proportional:

  Many things to do around, good restaurants, nice

coffee shops, bars, pubs, dancing places etc.   Inexpensive.

  Easy to travel to other beautiful cities which

are near, e.g., Philadelphia, New York City, Toronto.


You can feel all four seasons while in Pittsburgh


  Different Health Plans   Basic (<~1K for a year)   Enhanced   +Dental   +Vision

  CMU Health Services   UPMC   My personal experience so far has been more

than good.


  Most of the students live in Squirrel Hill,

Shadyside, Oakland.   Other neighborhoods: Bloomfield, Point Breeze, Regent Square.   If you do not own a car, make sure that you pick a place with many buses coming by from there (e.g., Squirrel Hill, Shadyside)   Great places to live for really good prices.


  An excellent environment for conducting

research, having pleasant breaks (great coffee on the 3rd floor), interesting research discussions on a whiteboard, Tea Parties.


Biking Canoeing Caving Swimming

Climbing Ski (Seven Springs Resort) with student prices ~15$

Hiking Kayaking

Tennis

Scuba Diving

Skydiving


Chess Club Gym Volleyball, Basketball Badminton Dancing


  Publications Travelling.   I have visited Belgium, France, Greece, Italy,

Stanford, New York.   Meeting Scientists from all over the world   Attend highly interesting talks (some of them).   Communicate your ideas, get people to learn about your research.   See and explore new places.


When you set out on your journey to Ithaca, pray that the road is long, full of adventure, full of knowledge…. …Always keep Ithaca in your mind. ToWelcome arrive theretoisCMU your ultimate and goal. Congratulations again! But do not hurry the voyage at all…


  Joseph Bradley for sharing previous year’s

slides.

  Jernej Barbic http://www-­‐rcf.usc.edu/~jbarbic/cmu-­‐start.html


Diane Stidle “Mother” of MLD


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