Foundations of Machine Learning Algorithms: Pen-Paper Calculations
"Foundations of Machine Learning (Algorithms): Pen-paper calculations " course is a non-coding course, which is a MUST for ALL persons desirous of learning Machine Learning from mathematical and algorithmic point of view. We will focus more on the theoretical aspects of the algorithms, parameters and hand-calculations will be done on dummy data step-by-step. In some cases, to automate the calclations, we will be using MS Excel.
- Clear algorithm explainations that help you to understand the principles that underlie each technique.
- The step-by-step algorithm workout on black-board to show you exactly how each model learns.
- Real worked examples so that you can see exactly the numbers in and the numbers out, there’s nowhere for the details to hide.
The Following algorithms are tentatively planned to be discussed and detailed tutorials/examples will be worked out in the class.
- Parametric and Non-parametric Machine Learning Algorithms
- The Supervised, Unsupervised and semi-supervised Learning
- The Bias-Variance Trade-off
- Overfitting and Underfitting
- Basic Definitions
- Even & odds of an event
- Bayes Theorem & applications
- Probability Distribution Functions
- Mean, Mode, Median
- Standard Deviation, Variance
- Correlation and Correlation-coefficient
- Standard Statistical Distributions
1. Identity Matrix and Diagonal Matrices
2. Transpose, Inverse, Trace, Norms and Determinant of Matrices
3. Symmetric & Orthogonal Matrices
4. Linear Independence and Rank
5. Eigenvalues and Eigenvectors of Symmetric Matrices
9. Matrix Multiplication
10. Operations and Properties
1. Gradients and Hessians of Quadratic and Linear Functions
2. Least Squares
3. Gradients of the Determinant
4. Eigenvalues as Optimization
- Gradient Descent.
- Linear Regression.
- Logistic Regression.
- Linear Discriminant Analysis.
- Classification and Regression Trees.
- Naive Bayes.
- K-Nearest Neighbors.
- Learning Vector Quantization.
- Support Vector Machines.
- Bagged Decision Trees and Random Forest.
- Boosting and AdaBoost.
|Dates||14th - 15th July, 21 - 22 July, 28 - 29th July 2018|
|Saturday Time :||6:00 PM - 9.00 PM|
|Sunday Time :||8:00 AM-11:00 AM + TUT 11:30 AM - 1:30 PM|