Machine Learning CS-433 – 2018 ‒ MLO ‐ EPFL

This is the OLD 2018 course website. For the current one, see here.

This course is offered jointly with the Information Processing Group.
Previous year’s website: ML 2017

Instructor	Martin Jaggi	Instructor	Ruediger Urbanke
Office	INJ 341	Office	INR 116
Phone	+41 21 69 35226	Phone	+41 21 69 37692
Email	[email protected]	Email	[email protected]
Office Hours	By appointment	Office Hours	By appointment

Teaching Assistants

Student Assistants

Lectures	Tuesday	17:15 – 19:00	Room: CO1
	Thursday	16:15 – 18:00	Room: SG1
Exercises	Thursday	14:15 – 16:00	Rooms: INF119 (A-Car), INJ218 (Cav-G), INF2 (H-Mo), INM202 (Mu-Sing), INR219 (Sinn-Z)

Language:		English
Credits :		7 ECTS

For a summary of the logistics of this course, see the course info sheet here (PDF).
(and also here is a link to official coursebook information).

Special Announcements

The final exam is scheduled for Tuesday 15.01.2019 from 16h15 to 19h15 in STCC08328
The first tuesday lecture will take place in the Rolex learning center for capacity reasons.
The new exercise sheet, as well as the solution (code only) for the previous week’s lab session will typically be made available each tuesday (here and on github).
Some old final exams: final 2017, solutions 2017, final 2016, solutions 2016
Some old mock exams: 2016,2015,2014
Projects: There will be two group projects during the course.
- Project 1 counts 10% and is due Oct 29th.
- Project 2 counts 30% and is due Dec 20th.
Labs and projects will be in Python. See Lab 1 to get started.
Code Repository for Labs, Projects, Lecture notes: github.com/epfml/ML_course
the exam is closed book but you are allowed one crib sheet (A4 size paper, both sides can be used), either handwritten or 11 point minimum font size; bring a pen and white eraser; you find the exam from last year with solutions posted a few lines above;
SOLUTIONS for exam; NOTE: There was a mistake in the grading of problem 14; the training error will increase as a function of lambda (not decrease) since the family will become simpler; this will be corrected globally; no need to ask us about this individually;

Detailed Schedule

(tentative, subject to changes)
Annotated lecture notes from each class are made available on github here.

Date	Topics Covered	Lectures	Exercises	Projects
18/9	Introduction (in RLC)	01a,01b
20/9	Linear Regression, Cost functions	01c,01d	Lab 1
25/9	Optimization	02a
27/9	Optimization		Lab 2
02/10	Least Squares, ill-conditioning, Max Likelihood	03a,03b		Project 1 details
04/10	Overfitting, Ridge Regression, Lasso	03c,03d	Lab 3
09/10	Cross-Validation	04a
11/10	Bias-Variance decomposition	04b	Lab 4
16/10	Research Talk, Transfer Learning & AutoML – Google Brain
18/10	Research Talk, Community detection and stochastic block models – Emmanuel Abbe		Q&A for proj.
23/10	Classification	05a
25/10	Logistic Regression	05b	Lab 5
30/10	Generalized Linear Models	06a		Proj. 1 due 29.10.
01/11	K-Nearest Neighbor	06b	Lab 6
06/11	Support Vector Machines	07a		Project 2 details
08/11	Kernel Regression	07b	Lab 7
13/11	Unsupervised Learning, K-Means	08a
15/11	K-Means, Gaussian Mixture Models	08b , 08c	Lab 8
20/11	Mock Exam
22/11	Gaussian Mixture Models, EM algorithm	09a	Mock Exam	solutions
27/11	Matrix Factorizations	10a
29/11	Text Representation Learning	10b	Lab 10
04/12	SVD and PCA	11a
06/12	SVD and PCA		Lab 11 + Q&A
11/12	Neural Networks – Basics, Representation Power	12a, 12b
13/12	Neural Networks – Backpropagation, Activation Functions	12c, 12d	Lab 12 + Q&A
18/12	Neural Networks – CNN, Regularization, Data Augmentation, Dropout	13a, 13b
20/12	Graphical Models – Bayes Nets	13c	Lab 13	Proj. 2 due 20.12.

Textbooks

(not mandatory)

Christopher Bishop, Pattern Recognition and Machine Learning
Kevin Murphy, Machine Learning: A Probabilistic Perspective
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning
Michael Nielsen, Neural Networks and Deep Learning

Projects

Final projects were done among 5 topic options.

The interdisciplinary ‘ML for Science’ projects performed this year across campus were:

Quality of Life in Swiss Cities based on OpenStreetMap
Compressive Sensing MRI using Deep Learning
Predicting the density of lightning activity from atmospheric and geographic features
Machine Learning for Science: Quantum Machine Learning
Solar Panel Recognition and Segmentation on Swiss Map using Convolutional Neural Networks
ML for crystal structure determination as an alternative to NMR spectroscopy
Human Behavior Modelling
Domain-invariant defect detection using deep learning
Comparing classification techniques for metabolic kinetic models selection
Spatially-Inferred Graphical Models for fMRI Data Mining
Crowded enzyme kinetics using simulation and machine learning
Quality of life in Swiss Cities
Ultrathin section segmentation
Correlations between cognitive performance and sensory stimuli in the work environment
Autism Diagnostic based on Machine Learning
Human performance modelling according to indoor temperature and light (quantity and colour)
Automatic Harmonization using Recurrent Neural Networks
Chord recognition on Beethoven string quartets
Machine Learning Privacy
Quality of Life Clustering of Swiss Cities from Insurance and Demographic Data
Machine learning for air quality measurement and modeling
The Case for Bagged Neural Networks: Evidence from Outlier Detection using Autoencoder Ensembles
Chord Prediction with The Annotated Beethoven Corpus
Predicting Forces on a Flapping Wing Model using Machine Learning
Brain Tissue Segmentation
Clustering and Predicting Swiss cities based on Insurance Data
Predicting the material properties of the arterial wall of a mouse
Predicting organic carbon with infrared spectra
3D Pointclouds Super-resolution for Digital humanities
Segmentation Of Silicon Wafers For Electron Microscopy Using Mask-RCNN
Classifying Nanopore Readings with Deep Learning
Wind Profile Prediction in an Urban Canyon: a Machine Learning Approach
A Stem Cell Classifier for Single Cell RNA Sequencing Data
Deep Convolutional Neural Networks for Cell Segmentation in Bright-Field Microscopy Images
Predicting Aerosol Particles: Sulfate, Nitrate and PM2.5
Healthy aging: age group prediction from chunking strategies during motor sequence learning
Multi-lingual text classifier for social media data
Implementation of an Improved Model for the Prediction of Effective Rate Constants in the Presence of Crowders
Architecture of Feelings
Evaluating the quality of videos through machine learning
Classifying segmentation defects in mutant zebrafish embryo
Fingerprinting DNS-over-HTTPS traffic
Statistics of Turkish researchers after the 2016 Coup d’Etat attempt
Analysis of the dismissal of Turkish researchers after the 2016 Coup d’Etat attempt
Evaluating the risk of relapse in melanoma

Links to the 3 official project competitions we offered on CrowdAI:

The following teams participated in the ICLR reproducibility challenge:

Improving Generalization and Stability of Generative Adversarial Networks
Meta-learning with differentiable closed-form solvers
A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
Detecting Adversarial Examples via Neural Fingerprinting
Learning Neural PDE Solvers with Convergence Guarantees
AutoLoss: Learning Discrete Schedules for Alternate Optimization
MAE : Mutual Posterior-Divergence Regularization for Variational Autoencoders
Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent