Francesco Corona

Teaching at AALTO/HUT and UFC

• Statistical data treatment (Aalto - CHEM-E7122 | In preparation, fall 2024)

• Analysis and simulation of stochastic reaction-diffusion systems (Aalto - CHEM-LV03 | 2022)

  Lecture notes:
  • Deterministic reaction networks
  • Degradation | Degradation and production
  • Higher-order reactions | Dimerisation, with production
  • Stochastic reaction networks | From Poissonian to diffusive evolutions
  • Stochastic differential equations | Chemical Fokker-Planck and Langevin equations
  • Models of molecular diffusion | Stochastic (advection-)reaction-diffusion processes
  Assignments:
  • E01 | E02 | E03 | E04 | E05 | E06

• Advanced process control (Aalto - CHEM-E7165/7225 | 2020 | 2021 | 2022 | 2023 | 2024)

  Recent lecture notes:
  • Intro: 2021 | 2022 | 2023 | 2024
  • Simulation: 2021) | 2022 | 2023 | 2024
  • Root finding: 2021 | 2022 | 2023 | 2024
  • Nonlinear programming (A): 2021 | 2022 | 2023 | 2024
  • Nonlinear programming (B): 2021 | 2022 | 2023 | 2024
  • Discrete-time optimal control: 2021 | 2022 | 2023 | 2024
  • Dynamic programming: 2021 | 2022 | 2023 | 2024
  • Continuous-time optimal control: 2021 | 2022 | 2023 | 2024
  • Hamilton-Jacobi-Bellman: 2021 | 2022 | 2023 | 2024
  • LQR from HJB: 2021 | 2022 | 2023 | 2024
  Recent assignments:
  • E01: 2022 | 2023 | 2024
  • E02: 2022 | 2023 | 2024
  • E03: 2022 | 2023 | 2024
  Seminars:
  • TO BE ADDED

• Process dynamics and control (Aalto - CHEM-E7140/7190 | 2019 | 2020 | 2021 | 2022 | 2023)

  Recent lecture notes:
  • Intro: 2019 | 2020 | 2021 | 2022 | 2023
  • Analysis: 2019) | 2020 | 2021 | 2022 | 2023
  • Dynamics: 2019 | 2020 | 2021 | 2022 | 2023
  • Linearisation: 2019 | 2020 | 2021 | 2022 | 2023
  • Linearisation example (A): 2019 | 2020 | 2021 | 2022 | 2023
  • Linearisation example (B): 2019 | 2020 | 2021 | 2022 | 2023
  • Linear dynamics: 2019 | 2020 | 2021 | 2022 | 2023
  • Linear controllers: 2019 | 2020 | 2021 | 2022 | 2023
  • Linear observers: 2019 | 2020 | 2021 | 2022 | 2023
  • Linear control w/ observer: 2019 | 2020 | 2021 | 2022 | 2023
  Recent exercises/assignments:
  • 2019: E01 | E02 | E03
  • 2020: E01 | E02 | E03
  • 2021: E01 | E02 | E03 | E04
  • 2022: E01 | E02 | E03 | E01x | E02x | E03x | E04x | E05x
  • 2023: E01 | E02 | E03 | E01x | E02x | E03x | E04x | E05x
  Seminars:
  • TO BE ADDED
  Exams:
  • 2019: T01
  • 2020: T01
  • 2021: T01
  • 2022: T01 | T02
  • 2023: T01 | T02
  • 2024: T01

• Topics in artificial intelligence (UFC - CK0146/0261, undergrad | 2016 | 2017 | 2019)

• Programming (UFC - CK0030, undergrad | 2017 | 2018 | 2019)

• Artificial intelligence (UFC - CK0031/0248, undergrad | 2016 | 2017 | 2018)

• Topics in machine learning (UFC - CK0255, undergrad | 2017 | 2018)

• Stochastic algorithms (UFC - CK0191, undergrad | 2018)

• Linear system theory (UFC - TIP7244, postgrad | 2018)

• Probability and random variables (UFC - TIP8241, postgrad | 2017)

• Pattern recognition (UFC - TIP8311, postgrad | 2015)

• System estimation and identification (UFC - TIP7048, postgrad | 2015)

• Information Visualization(HUT/Aalto - T-61.5010 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014)

• Multivariate Data Analysis for Chemometrics (HUT - T-61.6050 | 2008)

• Introductory Elements of Functional Data Analysis (HUT - T-61.6030 | 2007)

• Nonlinear Dimensionality Reduction (HUT - T-61.6050 | 2007)

Intensive at AALTO/HUT/UNICA

• Chemometric data analysis: Fundamental methods (Aalto | 2020)

  A series of lectures designed for the course `Advances in crystallization and crystal characterization techniques'
  
  
  • Calibration and simple linear regression
  • Multiple linear regression andf classical least-squares
  • Principal component analysis and regression

• Linear Methods for Quantitative Analysis in Light Spectroscopy (HUT - T-61.5050, 2007)

  A series of lectures designed with Elia Liitiäinen to the course `High-throughput bioinformatics'. The course was organized by Janne Nikkilä, Petri Auvinen and Leo Lahti
  

• Workshop on Functional Data Analysis (HUT - T-61.6900 | 2008)

  A series of lectures by Prof. Jim Ramsay. The course was organized jointly with Timo Hurme and Elia Liitiäinen

• Data-derived methods for process monitoring and supervision (UNICA | 2012)

• Nonlinear dimensionality reduction (UNICA | 2011)

Support material | Basics (in progress, just started adding the material here)

• Matrix algebra for engineers: Functions, Fields and vector spaces, Subspaces and basis, Linear maps, Matrix representations of linear maps, Change of basis, Norms, Induced norms, Orthogonality and adjoints, Hermitian matrices, SVD
• Linear algebra: TBA
• Probability theory: Elementary intuition, Measure theory, Probability concepts and tools, Independence, Dependence, Analysis
• Laplace transforms for engineers: Definitons and properties, Rational functions, Differential equations
• Circuits, signals and systems: Elements, Laplace and z-transforms, Convolution, System function and frequency response, Synthesis, Fourier series and integrals,
• Thermodynamics: TBA
• Machine learning: Learning theory and learnability, Learning methods, Advanced learning theory

April 2024.