Filip Panjevic is a Co-Founder and CTO at ydrive.ai - startup dealing with self-driving cars, and one of the founders of Petnica Machine Learning School.
Filip's talk will focus on the story of Petnica School, how did it start, what has changed since the beginning, how the concept of school looks right now and why is that concept good for making new data scientists. This talk will be perfect for people who consider starting their careers in the data science field!
4. 𝛙 : Machine Learning
10 day machine learning summer school
5. 𝛙 : Machine Learning
10 day machine learning summer school
Theory and hands-on experience with mentorship
6. 𝛙 : Machine Learning
10 day machine learning summer school
Theory and hands-on experience with mentorship
Teachers from Microsoft, MSR, Belgrade U, Cambridge U,
DeepMind, Google Brain, etc.
7. 𝛙 : Machine Learning
10 day machine learning summer school
Theory and hands-on experience with mentorship
Teachers from Microsoft, MSR, Belgrade U, Cambridge U,
DeepMind, Google Brain, etc.
300+ applicants each year from all over the world
8. 𝛙 : Machine Learning
10 day machine learning summer school
Theory and hands-on experience with mentorship
Teachers from Microsoft, MSR, Belgrade U, Cambridge U,
DeepMind, Google Brain, etc.
300+ applicants each year from all over the world
130+ graduates to date
9. 𝛙 : Machine Learning
10 day machine learning summer school
Theory and hands-on experience with mentorship
Teachers from Microsoft, MSR, Belgrade U, Cambridge U,
DeepMind, Google Brain, etc.
300+ applicants each year from all over the world
130+ graduates to date
91% of alumni believe PSI:ML influenced their education and
their careers
22. Ideal candidate
Undergrad or grad student
Interested in ML
Good with math
Likes to code/hack
Thinks outside the box
Can absorb a densely packed curriculum
Works well in a team
23. Ideal candidate
Undergrad or grad student
Interested in ML
Good with math
Likes to code/hack
Thinks outside the box
Can absorb a densely packed curriculum
Works well in a team
34. “Pairing lectures with workshops so it is
easier to grasp the theoretical
framework with hands on approach”
“Workshops had limited interactivity,
mostly came down to copy-pasting”
“Schedule too crammed - should
be at least 3-4 more days”
“Too diverse lectures - should be split
into basic and intermediate”
“We might consider two
sessions e.g. beginners and
advanced.”
“Projects should have started earlier so they had
more time to understand the problem and better
organize.”
36. Introduction to ML
Introduction to Data Science
Logistic Regression
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Random Decision Forests
Clustering
Principal Component Analysis &
Autoencoders
Support Vector Machines
Logistic regression workshop
Caffe workshop
Curriculum 2015
37. Introduction to ML
Introduction to Data Science
Logistic Regression
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Random Decision Forests
Clustering
Principal Component Analysis &
Autoencoders
Support Vector Machines
Bayesian data analysis
Backpropagation
Markov Chain Monte Carlo
Natural Language Processing
Supervised Learning Algorithms
Logistic regression workshop
Caffe workshop
Octave workshop
RNN workshop
Curriculum 2016
38. Introduction to ML
Logistic Regression
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Semantic Segmentation
Object Detection
Generative Adversarial Networks
Random Decision Forests
Clustering & Principal Component Analysis
Support Vector Machines
Bayesian data analysis
Backpropagation
Markov Chain Monte Carlo
Natural Language Processing
Supervised Learning Algorithms
Reinforcement Learning
Logistic regression workshop
Clustering & PCA workshop
Caffe workshop
Octave workshop
RNN workshop
NN workshop
GAN workshop
Curriculum 2017
39. Introduction to ML
Logistic Regression
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Semantic Segmentation
Object Detection
Generative Adversarial Networks
Random Decision Forests
Clustering & Principal Component Analysis
Support Vector Machines
Natural Language Processing
Supervised Learning Algorithms
Reinforcement Learning
SLAM
Machine Learning in Medicine
Logistic regression workshop
Clustering & PCA workshop
NN workshop
RNN workshop
GAN workshop
CNN workshop
RL workshop
NLP workshop
Curriculum 2018
40. Introduction to ML
Logistic Regression
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Random Decision Forests
Boosting
Gaussian Processes
Clustering & Principal Component Analysis
Support Vector Machines
Modern Natural Language Processing
Supervised Learning Algorithms
Reinforcement Learning
Geometric ML
Machine Learning in Medicine
Numpy Intro
TensorFlow intro
Logistic regression workshop
NN workshop
RNN workshop
GAN workshop
CNN workshop
RL workshop
NLP workshop
Curriculum 2019
48. Homework 2016
2x Outside-of-the-box problem
1x Pure engineering problem
Solutions submitted over email in Python,
C, C++, Octave...
Tested semi-manually
49. Homework 2017
2x Outside-of-the-box problem
1x Pure engineering problem
Solutions submitted through Petlja.org in
Python, C, C++, Octave...
Tested automatically through Petlja.org
50. Homework 2018
2x Outside-of-the-box problem
1x Pure engineering problem
Solutions submitted through Petlja.org in
Python, C, C++, Octave...
Tested automatically through Petlja.org
51. Homework 2019
2x Outside-of-the-box problem
1x Pure engineering problem
Solutions submitted through Petlja.org in
Python
Tested automatically through Petlja.org
Tasks from previous years available on
Petlja.org
54. 2019 Student Projects
Solving Rubik’s cube using RL
Learning to walk using RL
Generating Favicons using GAN’s
Generating Anime characters using GAN’s
Depth estimation from stereo
Vehicle egomotion estimation
55. “I went from not being accepted to PSI:ML to actually
becoming a lecturer there! Career wise – applying for the
Summer Institute turned out to be one of the best
decisions I've made – it jump-started me into neural
networks.”
-Bruno Gavranovic
56. “There’s a lot of content to go through and always
something to discuss, but don’t fret, it all starts from the
very basics of ML. So, even if you don’t have any idea
about anything, the seminar will pick you up at whatever
skill level you’re at.”
-Vladimir Nikolić and Leander Schröder
57. “Now I am a part of the Evoke team in Microsoft
Development Center Serbia, where I am seeing first
hand how ML is used every day for improving products
and user experience.”
-Natalija Radić
58. “The most important part of the seminar is the people.
There, you have a great opportunity to grow your
professional network and make new friends by meeting
a handful of hardworking and talented participants and
lecturers.”
-Marko Mihajlović and Nikola Popović