Foundations of Machine Learning Algorithms: PenPaper Calculations
" Foundations of Machine Learning (Algorithms): Penpaper calculations " course is a noncoding 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 handcalculations will be done on dummy data stepbystep. In some cases, to automate the calclations, we will be using MS Excel.
Course Contents:
The Following algorithms are tentatively planned to be discussed and detailed tutorials/examples will be worked out in the class.
 General Introduction:
 Parametric and Nonparametric Machine Learning Algorithms
 The Supervised, Unsupervised and semisupervised Learning
 The BiasVariance Tradeoff
 Overfitting and Underfitting

Essential Mathematics for Machine Learning  Part – A: Basic Probability
 Basic Definitions
 Even & odds of an event
 Bayes Theorem & applications
 Probability Distribution Functions
 Mean, Mode, Median
 Standard Deviation, Variance
 Correlation and Correlationcoefficient
 Standard Statistical Distributions
Essential Mathematics for Machine Learning  Part – C: Linear Algebra
 Matrix Multiplication
 Operations and Properties
 Identity Matrix and Diagonal Matrices
 Transpose, Inverse, Trace, Norms and Determinant of Matrices
 Symmetric & Orthogonal Matrices
 Linear Independence and Rank
 Eigenvalues and Eigenvectors of Symmetric Matrices
 Matrix Calculus
 Gradients and Hessians of Quadratic and Linear Functions
 Least Squares
 Gradients of the Determinant
 Eigenvalues as Optimization
 Linear Algorithms:
 Gradient Descent.
 Linear Regression.
 Logistic Regression.
 Linear Discriminant Analysis.
 NonLinear Algorithms:
 Classification and Regression Trees.
 Naive Bayes.
 KNearest Neighbors.
 Learning Vector Quantization.
 Support Vector Machines.
 Ensemble Methods:
 Bagged Decision Trees and Random Forest.
 Boosting and AdaBoost.
Course Highlights:
 Clear algorithm explainations that help you to understand the principles that underlie each technique.
 The stepbystep algorithm workout on blackboard 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.
Training Schedule:
SaturdaySunday Batch6 Days  17, 24 Sept.; 1, 7, 8, 14 October 2017 
Saturday Time:  9:30 AM  1:00 PM 
Sunday Time:  8:00 AM – 11:00 AM 
After doing this course you would also like to register for Machine Learning in MATLAB, and/or Machine Learning in Python, and Deep Learning with Python courses.
All the information regarding Fees, discounts and Registration Form is available here.
Page Last Updated: Saturday 02Sep2017 02:48:42 IST