My postgraduate offer for 2017/1: Designing IT Research for Impact

I’ll offer, next semester, the subject “IA376 — Topics in Computer Engineering VII — Class D: Designing IT Research for Impact” to the postgraduate students at FEEC/UNICAMP. That will be a 4 credits (60 hours) subject.

When: Tuesdays and Thursdays, from 19 to 21h

Where: FEEC/UNICAMP, Post-graduate Hall, Room PE-24.

We aspire our research to impact the world beyond h-indices and impact factors — we dream that our work, directly or indirectly, improves people’s lives. I start with a tease: do you have any evidence that this is the case for your research? My aim is for us to learn to ask that question, and then to give it a fruitful answer.

Let’s mine academic sources, government policies, NGO/Charities experiences, think-tank statements to answer the following questions:
(1) How can I design research in IT to maximize social impact?
(2) How can I work with the intended communities to to favor that impact?
(3) How can I measure (or at least inspect) that impact?

Our scope will be limited to Information Technology (i.e., “a GIS to improve access to clean water” is within scope, “a new polymer to clean water” not so much), and to disfavored communities (i.e., “real-time social networks to improve sanitization” is within scope, “real-time social networks to exchange designer watches” not so much).

This course will be nothing like your typical classroom experience. There will be no lectures. We will meet for the presencial sessions to discuss previous work, and plan our attack for the next week. I’ll expect you to continue working throughout the week. There will be no exams: I’ll grade your work based on participation during the sessions, progress between sessions, self assessment, and peer assessment. Active participation will be mandatory. This means (surprise!) talking in public. Everyone will be learning together, so all of us must accept the risk to be wrong. This course won’t work for those who always want to appear wise and knowledgeable. Group work will be highly encouraged. The course will be in English.

We’ll be a cozy small group: at most 18 students. I’ll select the candidates based on a letter of intentions, and on previous experience. Write a short e-mail to dovalle@dca.fee.unicamp.br with a short-résumé (American style) summarizing your research, professional, and extra-curricular activities, highlighting experience relevant for the course; add also a short statement (300~600 words) explaining your current research, or future research that you’d like to develop, highlighting the social impact you’d like to see. We will be particularly interested in IT research for Health and Education benefiting disfavored communities, but other themes are welcome.

Attention to the procedures and deadlines: Whether you are a regular or special-enrolled student, please send me your application by e-mail (as explained above) until 8 January 2017. You’ll have also to make your enrollment as usual. If you want to enroll as a “special-enrolled student” act now: pre-enrollment closes on December  7th! Regular students can follow the usual enrollment calendar.

More info at FEEC’s Postgraduation Page.

 

My postgraduate offer for 2016/1 : Deep Learning From a Statistician’s Viewpoint

With few exceptions, my postgraduate offers follow a pattern. On the second semester, I offer my “101” Multimedia Information Retrieval course, which introduces multimedia representations, machine learning, computer vision, and… information retrieval. On the first semester, I offer a topics course, usually following a book : so far we have explored Bishop’s PRML, Hofstadter’s GEB, and Jaynes’ “Probability Theory”.

For 2016/1, I’m risking something different :

“Artificial Intelligence is trending again, and much of the buzz is due to Deep Neural Networks. For long considered untrainable, Deep Networks were boosted by a leap in computing power, and in data availability.

Deep Networks stunned the world by classifying images into thousands of categories with accuracy, by writing fake wikipedia articles with panache, and by playing difficult videogames with competence.

My aim here is a less “neural” path to deep models. Let us take the biological metaphors with a healthy dose of cynicism and seek explanations instead in statistics, in information theory, in probability theory. Remember linear regression ? Deep models are multi-layered generalized linear models whose parameters are learned by maximum likelihood. Let us start from there and then explore the most promising avenues leading to the current state of the art.

This course will be nothing like your typical classroom experience. There will be no lectures. We will meet once a week for a presencial session to discuss previous work, and plan our attack for the next week. I’ll expect you to continue working throughout the week. There will be no exams. I’ll grade your work based on participation during the sessions, progress between sessions, self assessment, and peer assessment.

Active participation will be mandatory. This means (surprise !) talking in public. Everyone will be learning together, so all of us must accept the risk to be wrong. This course won’t work for those who always want to appear wise and knowledgeable. The course will be in English.

Deep networks can be seen as hierarchical generalized linear models.

Deep networks can be seen as hierarchical generalized linear models.

We’ll be a cozy small group : at most 12 students. I’ll select the candidates based on a letter of intentions, and on previous experience. Write a short e-mail to dovalle@dca.fee.unicamp.br. No need to be fancy : just state your reasons for participating, and any previous experience (academic, professional, and extra-curricular) with Machine Learning, Statistics, Probability, or Information Theory.

This course is not for beginners, nor for the faint of heart. We are jumping in head first at the deep (tee hee !) end. After all, we will delve into one of the most engaging intellectual frontier of our time. I dare you to join us !”

Very important ! If you want to enroll at this course without being enrolled at the program (what UNICAMP awfully calls “special students”), you have to do you pre-enrollment until 7/Dec/2015 (hard deadline !). Even if you are enrolled at the program (“regular student”) send me your application at most until 31/Dec/2015, because I’ll select regular and special (urgh !) students at the same time.

EDIT 20/01 : I have sent the acceptance notices — looking forward to work with a swell group of very motivated students !

What : Post-graduate course for the Master or Doctorate in Electrical Engineering program of UNICAMP (4 credits)

When : 2016/1st semester — mandatory presencial meetings Tuesdays from 19 to 21h ; support meetings same day from 16 to 18h

Image credit : composite from Ramón y Cajal 1st publication showing a cerebellum cut, and scatterplots from Fisher’s iris dataset drawn by Indon~commonswiki, wikimediacommons.

Summer Reading — Around GEB

I have already written about my fascination with Hofstadter’s Gödel, Escher, Bach (fascination that I share with many practitioners of AI). Although I am, relatively, a latecomer, ever since I’ve read the book, my enchantment with it has never ceased to grow.

The book will be turning 35 next May, and in order to celebrate the occasion, I am very pleased to offer the postgraduate students the course “35 years of Gödel, Escher, Bach — Commented and Extended Reading of a Masterpiece of Artificial Intelligence”. With its philosophical slant, that course is a major depart from my usual offerings, which are focused on technical aspects of Statistical Learning and Multimedia Information Retrieval. I asked my colleagues on other departments (Linguistics, Social Sciences, Philosophy, etc.) to diffuse the syllabus among their students, and I am crossing my fingers that I’ll have a diverse classroom. My promise was :

“The target audience are postgraduate students in all knowledge areas, interested in Artificial Intelligence and Philosophy of Mind. I will not assume any previous knowledge, neither in Philosophy, neither in Logic, nor in Computer Sciences. During the course we will brave advanced topics of Computing Theory and untangle very hairy Logic and Math : if you’re afraid of those stuff, this will be a great opportunity of losing your fear.”

The course is also an opportunity for me to attack themes of Metalogic and Metamathematics, which I love, but seldom have the opportunity to study in earnest. They say that the best way to rest the mind is to work on different problems — and this is exactly what I’m doing during the short end-of-year University recess. I’ve put my studies in Statistics, and Computational Geometry on hold, escaped to the country, and brought another stack of books with me :

Summer Reading — The Annotated Turing (Petzold); Undecidable Theories (Tarski); From a Logical Point of View (Quine); Theory dos Conjuntos: Um Mínimo (Miraglia); The Mathematics of Logic (Kaye); Roads do Infinity (Stillwell); Quine — A Guide for the Perplexed (Kemp); Gödel's Proof (Nagel, Newman); Gödel, Escher, Bach: an Eternal Golden Braid (Hofstadter)

 

By the by, I wish everyone a great 2014 !