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CMSC 173

Machine Learning

Fundamentals of machine learning algorithms and applications

Course Overview
📖
Course Syllabus
Schedule, grading, policies & resources
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Fundamentals 5 modules
intro
Welcome to CMSC 173
Module intro
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1
Introduction to Machine Learning
Module 1
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2
Parameter Estimation
Module 2
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3
Linear Regression
Module 3
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4
Regularization
Module 4
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Model Selection & Validation 3 modules
5
Exploratory Data Analysis
Module 5
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6
Model Selection
Module 6
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7
Cross Validation
Module 7
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Classification Methods 4 modules
8
PCA
Module 8
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9
Logistic Regression
Module 9
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10
Classification
Module 10
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11
Kernel Methods
Module 11
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Deep Learning 3 modules
12
Clustering
Module 12
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13
Neural Networks
Module 13
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14
Advanced Neural Networks
Module 14
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📋
ML Research Project — Up to 5 members per group · 1st Sem 2026-2027
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Weekly Lab Exercises

Practice exercises for each week's lecture (30-60 min each).

1
Week 1 Lab
Welcome Lab: What You Already Have
2
Week 2 Lab
Parameter Estimation & the Bias–Variance Tradeoff
3
Week 3 Lab
Linear Regression: Gradient Descent from Scratch
4
Week 4 Lab
Regularization: Ridge, Lasso & Choosing Lambda
5
Week 5 Lab
Exploratory Data Analysis with pandas
6
Week 6 Lab
Model Selection & Evaluation: Metrics & ROC
7
Week 7 Lab
Cross-Validation & Hyperparameter Tuning
8
Week 8 Lab
PCA from Scratch: Eigenvectors & Scree Plots
9
Week 9 Lab
Logistic Regression from Scratch
10
Week 10 Lab
Classification: KNN, Naive Bayes & Decision Trees
11
Week 11 Lab
Kernel Methods & Support Vector Machines
12
Week 12 Lab
Clustering: K-Means from Scratch
13
Week 13 Lab
Neural Networks: A 2-Layer Net from Scratch
14
Week 14 Lab
Under the Hood of Modern AI: Convolution & Attention

💡 Tip: Open in Colab → File → Save a copy in Drive to keep your work. Try exercises first, then check solutions to verify your approach.