EE 514 (CS 535) Machine LearningSpring 2023
Syed Babar Ali School of Science and Engineering

Homework  Solutions 
Homework 01  Solutions 
Homework 02  Solutions 
Homework 03  Solutions 
Homework 04  Solutions 
Week 01 (Notes 01)
Course Introduction
Machine Learning Overview
Supervised Learning: Formulation, Setup, Traintest split, Generalization
Week 02 (Notes 02)
kNearest Neighbor (kNN) Algorithm, Algorithm Formulation
Distance Metrics, Choice of k, Algorithm Convergence, Storage, Time Complexity Analysis, Fast kNN
Week 03 (Notes 03)
The Curse of Dimensionality and Connection with kNN
Dimensionality Reduction: Feature Selection and Extraction, Principal Component Analysis
Week 04 (Notes 04)
Classifer Performance Evaluation: Confusion Matrix Sensitivity, Specificity, Precision Tradeoffs, ROC, AUC, F1Score and Matthew’s Correlation Coefficient
Week 05 (Notes 05)
Multiclass Classification, Evaluation, Micro, Macro Averaging
Regression: Linear Regression, Polynomial Regression, Overfitting
Regularization
Week 06
Gradient Descent Algorithm (see Week 05 Notes)
Probability Review (Notes 06)
Bayesian Learning Overview (see Week 07 Notes)
Week 07
Bayesian Learning Framework, MAP and ML Hypothesis
Linear Regression as ML estimation
Naive Bayes Classifier (Notes 07)
Naïve Bayes Classifier for Text Classification (Week 0801)
Bayesian Networks Introduction (see Week 05 Notes)
Week 08
Logistic Regression: Mathematical Model, Decision Boundaries, Loss/Cost Function, Gradient Descent
Multiclass Logistic Regression (Notes 09)
Week 09
Perceptron and Perceptron Classifier, Perceptron Learning Algorithm and its Geometric Intuition
Perceptron Learning Algorithm Convergence (Notes 10)
SVM Overview
Week 10
Hard SVM, Soft SVM, Kernel Trick (Notes 11)
Week 11
Neural Networks Introduction, Model, Forward Pass (Notes 12)
Week 12
Neural Networks: Back Propagation (See Notes 12)
Week 13
Unsupervised Learning, Clustering Overview
Kmeans Clustering
Agglomerative Clustering (Notes 13)
Week 14
Introduction to Deep Learning and Convolutional Neural Networks (Notes 14)