My notes (slides) during my PhD study in UMONS since Feb-2017, email me if you catch a mistake/typo
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Tutorial/Lecture material

On Gradient descent solving quadratic problems On Coordinate descent solving quadratic problems (under construction) Linear Algebra recap

Continuous Optimization

  • General Acceleration Strategies for optimization algorithms

    • Acceleration by extrapolation or linear combination of sequence

    • Acceleration by domain transformation : preconditioning

    • Acceleration by subset sampling : radomization approach, multi-grid approach, safe feature removal approach

    • Acceleration by hardware : parallization and distributived computing

Nonnegative Matrix Factorizations : heuristics, algorithms, theory

Matrix Completion

Linear Algebra / Matrix Theory

Randomized Linear Algebra, Compressive Sensing, Random Matrix

Multilinear/Tensor Algebra and Tensor methods

  • Algebra

    • Fundamentals of Tensor

    • Tensor shortcut : \(kr(U,V)^\top kr(U,V) = U^\top U \odot V^\top V\)

    • Canonical Polyadic Decomposition

    • The MTTKRP bottleneck

    • Tensor method (3rd order method) for optimisation

  • Theory

    • Why tensor : “easier” to get uniqueness of decomposition

Machine Learning

  • Machine Learning applications

    • Hyper-spectral imaging

    • Audio source separation

    • Electricity load profiling

    • Fluorescence spectroscopy

    • Gas Chromatography Mass Spectrum

    • Unsupervised Representation Learning

On software engineering

  • Version control

  • On efficient coding on experiments comparing multiple algorithms

  • On using LaTeX

  • On writing static webpage