Live lectures over Zoom
Recorded and posted for review
Prerequisite: Linear Algebra, CS 233
Algorithms of Machine Learning
Spring 2024
A survey of machine learning algorithms and tools, with *guest lectures.
Syllabus (subject to change)
- next→ Aug 22: Algorithms of Machine Learning
- Aug 24: Review: Linear Algebra, Probability - Shawn Solberg
- Aug 29: Simple Linear Regression
- Aug 31: Linear Regression - Gradient Descent - Alex Frey
- Sep 05: No class - Labor Day
- Sep 07: Supervised vs Unsupervised
- Sep 12: Logistic Regression- Sean Stevenson
- Sep 14: Model Evaluation Metrics
- Sep 19: Improving your model - Feature Engineering- Erik Akert
- Sep 21: Lifecycle of a Model
- Sep 26: Support Vector Machine* - Ryan Pacheco
- Sep 30: SVM - Kernel Trick* - Josh Johnston
- Oct 03: Decision Trees
- Oct 05: Decision Trees splitting criteria* - Erik Akert
- Oct 10: Ensemble Models - Random Forests* - Dr Matthew Jones
- Oct 12: Midterm w/ Pizza!
- Oct 17: Principal Component Analysis* - Eric Freiling
- Oct 19: Bayesian Networks* - Dr Casey Kennington
- Oct 24: Reinforcement Learning* - Dr Casey Kennington
- Oct 26: KMeans Clustering* - Will Richmond
- Oct 31: Deep Learning - Perceptron
- Nov 02: Deep Learning - Image Classification* - Gerardo Caracas
- Nov 07: Anomaly Detection* - Dr Nate Monnig
- Nov 09: Sequential Modeling* - Erik Akert
- Nov 14: Model drift factors
- Nov 16: Model interoperability
- Nov 21: No class - Thanksgiving Break
- Nov 23: No class - Thanksgiving Break
- Nov 28: Scaling machine learning - Noah Pritikin
- Nov 30: Ethics in machine learning* - Dr Ekstrand
- Dec 05: Presentations in Class
- Dec 07: Presentations in Class
- Dec 12 Midnight to midnight : Final Online Google Form
Homework
Grading
- 50% Homeworks
- 20% Midterm
- 20% Final
- 10% Pop Quizzes
References (useful links)