EE 514 (CS 535) Machine LearningSpring 2023
Syed Babar Ali School of Science and Engineering
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Homework | Solutions |
Homework 01 | Solutions |
Homework 02 | Solutions |
Homework 03 | Solutions |
Week 01 (Notes 01)
Course Introduction
Machine Learning Overview
Supervised Learning: Formulation, Setup, Train-test split, Generalization
Week 02 (Notes 02)
k-Nearest 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
Classifer Performance Evaluation: Confusion Matrix Sensitivity, Specificity, Precision Trade-offs, ROC, AUC, F1-Score and Matthew’s Correlation Coefficient
Week 04 (Notes 04)
Multi-class Classification, Evaluation, Micro, Macro Averaging
Regression: Linear Regression, Polynomial Regression, Overfitting
Week 05
Regularization (see Week 04 Notes)
Week 06
Gradient Descent Algorithm (see Week 04 Notes)
Week 07 (Notes 05)
Bayesian Learning Framework, MAP and ML Hypothesis
Logistic Regression: Mathematical Model, Decision Boundaries, Loss/Cost Function, Gradient Descent
Multi-class Logistic Regression
Linear Regression as ML Estimation
Week 08 (Notes 06)
Naive Bayes Classifier
Naïve Bayes Classifier for Text Classification
Bayesian Networks Introduction
Week 09 (Notes 07)
Perceptron and Perceptron Classifier, Perceptron Learning Algorithm and its Geometric Intuition
Perceptron Learning Algorithm Convergence
Week 10
Hard SVM, Soft SVM, Kernel Trick (Notes 08)
Week 11 (Notes 09)
Neural Networks Introduction, Model, Forward Pass
Neural Networks: Back Propagation
Week 12 (Notes 10)
Unsupervised Learning, Clustering Overview
K-means Clustering
Agglomerative Clustering
Week 13
Decision Trees (Notes 11)
Bagging, Random Forest and Boosting
Week 14
Introduction to Deep Learning and Convolutional Neural Networks (Notes 12