👩🏻🏫 I am a professional in Mathematics and Computer Science with more than 10 years of experience teaching classes from the youngest to a university level.
👩🏻💻 I consider myself a person who is passionate about Mathematics and Technology, as well as patiently helping those who are interested in navigating the waters of science.
📸 🏃🏻♀️ 🏞 📚 🧘🏻♀️ Besides Mathematics I love many other things, among my hobbies are photography, running, hiking and reading; I can also share my knowle...
👩🏻🏫 I am a professional in Mathematics and Computer Science with more than 10 years of experience teaching classes from the youngest to a university level.
👩🏻💻 I consider myself a person who is passionate about Mathematics and Technology, as well as patiently helping those who are interested in navigating the waters of science.
📸 🏃🏻♀️ 🏞 📚 🧘🏻♀️ Besides Mathematics I love many other things, among my hobbies are photography, running, hiking and reading; I can also share my knowledge on these topics with great pleasure.
✍🏽 🙌🏼 Our process would focus on learning through practice, so my classes will adjust to the rhythm of the learner, sharing the theoretical-practical aspects with the appropriate instructional material. This will allow you live guided problem solving where you can clarify your doubts immediately with me.
💪🏼 💯 📈 I would love to share my knowledge and learning techniques through my classes, to help you improve your motivation, skills and abilities in Mathematics. Mathematics is everywhere and it is much more beautiful than what we have been made to see many times, so go ahead!
📌 Here is a list of topics that could help you, and if you do not see any that interest you in this list remember to ask, networking often gives good results in searches!
- Basic mathematics at primary and secondary levels (5 years to 17 years):
- Natural numbers and operations
- Fractional numbers and operations
- Polynomials in the Cartesian plane
- Integers and operations
- Number and variable
- Basic calculus university level (18 years and older):
- Functions, limits and continuity
- Derivative and differentiation
- Graphing methods
- Integration
- Integration techniques
- Vectors, lines, planes and surfaces
- Differential calculus of functions of more than one variable
- Multiple integration
- Calculation of vector fields
- Elementary algebra and its operations
- Linear algebra
- Vectors
- Matrix calculus
- Vector spaces
- Linear transformations
- Numerical linear algebra
- LU factorization
- QR factorization
- Eingevalues
- Discrete Mathematics I (Logic)
- Propositional Logic
- Predicate Logic
- Set Theory
- Mathematical Induction
- Discrete Mathematics II
- Linear Recurrence (1st and 2nd Order)
- Binary Relationship
- Equivalence relation
- Partial and total order relationship
- Algebraic Structures (Groups, Homomorphisms and Isomorphisms)
- Boolean algebra
- Directed and undirected graphs
- Probability and statistics
- Random Experiment, Sample Space, Probabilistic Model, Laplace's Law, Combinatorics.
- Conditional probabilities, Independence, Rule of addition and multiplication, Total probabilities and Bayes' Theorem.
- Random variables: discrete and continuous, probability function, cumulative probability function, variance and expectation
- Joint Random Variables: Marginals and Independence. Covariance and Correlation Coefficient.
- Central Limit Theorem: Case sum, proportion and average. Law of large numbers.
- Confidence intervals: for the mean and proportion with known and unknown variance.
- Hypothesis testing
- Markov chains: classification of states, irreducible chains and limit probability vector.
- Descriptive statistics: mean, mode, median, percentiles and histograms.
- Machine Learning Algorithms and Models (Supervised and Unsupervised)
- Linear Regression (Least Squares, Ridge, Lasso and Polinomial)
- Logistic Regression
- K-Nearest Neighbours
- Overfitting y Underfitting
- Support Vector Machines
- Cross Validation
- Decision Trees
- Confusion Matrix and Basic Evaluation Metrics
- Naive Bayes Classifiers
- Random Forest
- Gradient Boosted Decision Trees
- Neural Networks
- Jupyter Notebooks en Python o R
