EE212 Mathematical Foundations for Machine Learning and Data ScienceFall 2021
Department of Electrical Engineering

Assignment  Solutions 
Assignments 01  Solutions 
Assignments 02  Solutions 
Assignments 03  Solutions 
Assignments 04  Solutions 
Programming Assignment 0 (PreLab, must be completed before Lab 01)
Programming Assignment 04 (Datasets available on LMS)
Weeks 01 and 02 (Lecture Notes)
Course Introduction
Operations on vectors: linear combination, norm, inner prooduct, angle, distance, correlation coefficient
Span, basis, linear independence, orthonormal vectors, vector spaces, GramSchmidt orthogonalization
Weeks 03 and 04 (Lecture Notes)
Matrices Notation, Application Examples and Basic Operations
Matrixvector product, Interpretations, Application Examples,Matrixmatrix product
Systems of Linear Equations, Formulation, Inverses, Leftinverse, Rightinverse, Inverse, Pseudoinverse, Connection with the linear equations
Weeks 05 and 06 (Lectures 0913)
EigenValue Decomposition (EVD) (Lecture Notes)
Curse of Dimensionality and Principal Component Analysis (Application of EVD) (Lecture Notes)
Singular Value Decomposition (SVD) (Lecture Notes)
Leastsquares Formulation (Lecture Notes)
Weeks 07 and 08 (Lectures 1416)
Calculus module: Functions, Derivatives, Gradient, Hessian, Jacobian, AntiDerivatives (Lecture Notes)
Week 09, 10 and 11 (Lectures 1721)
Probability Theory overview, Probability models, Axioms of probability, Conditional probability Bayes theorem, Law of total probability (Lecture Notes)
Independence, Combinatorics
Discrete random variable, probability mass function, Continuous random variable, probability density function (Lecture Notes)
Introduction to Inference (Lecture Notes)
Week 12 (Lectures 2224)
Overview of supervised learning, ML nomenclature, problem setup and traintest split (Lecture Notes)
Week 13 (Lectures 2526)
kNN Algorithm: Overview and Analysis (Lecture Notes)
Analysis and Evaluation of Classifierâ€™s Performance (Lecture Notes)
Week 14 (Lectures 2728)
Overview of Perceptron Classifier, Logistic Regression and Neural Networks (Lecture Notes)