Magdalena Bouza

Hi! I'm Magui (or Maggie) and I joined DeepLearning.AI as a Curriculum Engineer. I've been teaching in the University of Buenos Aires for 7 years now, and in the last year, I've also started teaching at a master's degree program from the same university. I mostly teach subjects related to probability and statistics, and derived applications. I studied electronic engineering, and currently find myself working to finish my Doctorate degree. Additionally I have some experience in consulting, oriented to bringing data science solutions to different clients. While I'm no stranger to teaching, online learning is pretty new to me and thus represents an exciting challenge! I see this as an incredible opportunity to improve myself and how I envision teaching. I live with my partner and our two adorable cats in Buenos Aires. Besides working, which currently takes quite a bit of my time, I love traveling and meeting new people. I also enjoy cooking very much, as well as trying new dishes from different cuisines. As a hobby I practice aerial dance, where I'm able to run on walls and pretty much fly!

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Linear Algebra for Machine Learning and Data Science (Coursera)

Apr 22nd 2024
Linear Algebra for Machine Learning and Data Science (Coursera)
Course Auditing
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After completing this course, learners will be able to: represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc.; apply common vector and matrix algebra operations like dot product, inverse, and determinants; express certain types of matrix operations as linear [...]

Calculus for Machine Learning and Data Science (Coursera)

Apr 22nd 2024
Calculus for Machine Learning and Data Science (Coursera)
Course Auditing
Categories
Effort
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After completing this course, learners will be able to: analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients; approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods; visually interpret [...]