Machine Learning CS-433

This course is offered jointly with the Information Processing Group.
Previous year’s website: ML 2018

Contact us: Use the moodle discussion forum, or some of the contact details below:

Instructor Martin Jaggi Instructor Ruediger Urbanke
Office INJ 341 Office INR 116
Email [email protected] Email [email protected]
Office Hours By appointment Office Hours By appointment
Teaching Assistants

  • Okan Altingövde
  • Grzegorz Gluch
  • Semih Günel
  • Prakhar Gupta
  • Anastasia Koloskova
  • Jessica Lanini
  • Andreas Maggiori
  • Aswin Suresh
  • Thijs Vogels
  • Yubo Xie
  • Teresa Yeo
Student Assistants

  • Sami Ben Hassen
  • Pei Wang
  • Wanhao Zhou
  • Clément Charollais
  • Iuliana Voinea
Lectures Tuesday 17:15 – 19:00 Room: SG1
Thursday 16:15 – 18:00 Room: SG1
Exercises Thursday 14:15 – 16:00 Rooms: INF119INF2INJ218INM202INR219
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 first week lectures (both tue+thur) 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 28th.
    • Project 2 counts 30% and is due Dec 19th.
  • 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

Detailed Schedule

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

Date Topics Covered Lectures Exercises Projects
17/9 Introduction (in RLC), Linear Regression 01a,01b
19/9 Cost functions, Optimization (in RLC) 01c,01d Lab 1
24/9 Optimization
26/9 Least Squares, ill-conditioning, Max Likelihood
01/10 Overfitting, Ridge Regression, Lasso Project 1 details
03/10 Cross-Validation, Bias-Variance decomposition
08/10 Classification
10/10 Logistic Regression
15/10 Generalized Linear Models
17/10 K-Nearest Neighbor
22/10 Support Vector Machines
24/10 Kernel Regression
29/10 Unsupervised Learning, K-Means Proj. 1 due 28.10.
31/10 K-Means, Gaussian Mixture Models
05/11        Mock Exam Project 2 details
07/11 Gaussian Mixture Models, EM algorithm Mock Exam
Matrix Factorizations
14/11 Text Representation Learning
19/11 SVD and PCA
21/11 SVD and PCA
26/11 Neural Networks – Basics, Representation Power
28/11 Neural Networks – Backpropagation, Activation Functions Q&A
03/12 Neural Networks – CNN, Regularization, Data Augmentation, Dropout
05/12 Neural Networks – Generalization  (t.b.d.)
11/12 Graphical Models – Bayes Nets
13/12 Graphical Models – Factor Graphs
17/12 Graphical Models – Inference and Sum-Product Algorithm
19/12 Research Talk (t.b.d.) Proj. 2 due 19.12.

Textbooks

(not mandatory)

Gilbert Strang, Linear Algebra and Learning from Data
Christopher Bishop, Pattern Recognition and Machine Learning
Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning

Michael Nielsen, Neural Networks and Deep Learning

Projects

Final projects last year were done among 5 options.

The interdisciplinary ‘ML for Science’ projects performed last 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 AIcrowd:
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