Mathematics for Machine Learning

Course Information

1. Lecturer's Information:

   - Lecturer/TD/TP: Dr. Khadra Bouanane 
   - Contact: bouanane.khadra@univ-ouargla.dz
   - Office Hours: Monday: 8 am-12 am, Wednesday: 8 am-12 am.

2.  Course Description:

   This course comprehensively introduces the mathematical foundations of Artificial Intelligence and machine learning. Topics include linear algebra, analytic geometry, and matrix factorization.

3. Learning Objectives:

   - Develop a solid understanding of linear algebra concepts and their use in ML.
   - Introduce concepts of analytic geometry principles and specific linear transformations extensively used in ML and A.I.
   - Explore matrix factorization techniques and their role in data analysis and A. I. applications.

4. Prerequisites

   Basic Knowledge of Calculus and Algebra

5. References

   - Deisenroth, M. P., Faisal, A. A., Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
   -Bishop, C.M. (2006). "Pattern Recognition and Machine Learning." 
   -Lay, D.C., Lay, S.R., and McDonald, J.J. (2015). "Linear Algebra and Its Applications.
   -Ross, S.M. (2000). "Introduction to Probability Models."
   -Boyd, S., and Vandenberghe, L. (2004). "Convex Optimization."
   -Goodfellow, I., Bengio, Y., and Courville, A. (2016). "Deep Learning."

6. Grading:

   -  Assessments: 70%
   - Homework: 20%
   - Class Participation: 10%

7. Schedule:

   Weeks 1-3:  Linear Systems of Equations. Unique Solutions and General Solutions. Gaussian Elimination, Solving Linear Systems of Equations. Weeks 4-5: Vector Spaces, Subspaces. Linear Independence, Rank, and Basis. Linear Applications. Affine Subspaces, Affine Applications. Weeks 6-8: Norms, Bilinear Forms, Dot Products. Distance and Lengths, Positive Definite Matrices. Orthogonality, Orthonormal Bases. Weeks 9-10: Orthogonal Projection, Projection onto an Affine Subspace, Rotations. Weeks 11-13: Matrix Decomposition and Factorization. L.U. Decomposition, SVD Decomposition.