Lecture Schedule

This is a tentative schedule. It is subject to change.

Date	Lecture	Notes	Homework
Week 1	—	—	—
Sep 2	01 – Course Intro and Git	Class Intro, Git
Sep 4	02 – Intro to Pandas and SciKit-Learn	Pandas, SciKit-Learn
Week 2	—	—	—
Sep 9	03 – Spark Project Pitch Day (Tentative)
Sep 11	04 – Probability & Linear Algebra Refresher	Probability, Linear Algebra
Week 3	—	—	—
Sep 16	05 – Distance, Similarity Functions and Time Series	Distances, Time Series
Sep 18	06 – Clustering I: k-means	k-means
Week 4	—	—	—
Sep 23	07 – Clusterin II: In Practice	Clustering in Practice
Sep 25	08 – Clustering III: Hierarchical	Hierarchical Clustering
Week 5	—	—	—
Sep 30	09 – Clustering IV: GMM and Expectation Maximization	GMM and EM
Oct 2	10 – Classification I: Decision Trees and Random Forests	Decision Trees
Week 6	—	—	—
Oct 7	11 – Classification II: k-Nearest Neighbors	k-NN
Oct 9	12 – Classification III: Naive Bayes and SVM	Naive Bayes and SVM
Week 7	—	—	—
Oct 14	🍂 No Class – Monday Schedule 🍂
Oct 16	13 – SVD Low Rank Approximations	SVD
Week 8	—	—	—
Oct 21	14 – Dimensionality Reduction: PCA and t-SNE	PCA and t-SNE
Oct 23	15 – Linear Regression	Linear Regression
Week 9	—	—	—
Oct 28	16 – Logistic Regression and Regularization	Logistic Regression
Oct 30	17 – Neural Networks I: Gradient Descent	NN I
Week 10	—	—	—
Nov 4	18 – Neural Networks II: Backpropagation	NN II
Nov 6	19 – Neural Networks III: CNNs	NN III
Week 11	—	—	—
Nov 11	20 – Recommender Systems	Recommender Systems
Nov 13	21 – Graphs I	Graphs I
Week 12	—	—	—
Nov 18	22 – Graphs II	Graphs II
Nov 20	23 – Time Series Analysis	Time Series
Week 13	—	—	—
Nov 25	24 – Intro to NLP	NLP
Nov 27	🦃 No Class – Thanksgiving Recess 🌽
Week 14	—	—	—
Dec 2	25 – Lecture to be announced
Dec 4	📽️ Project Presentation I 📽️
Week 15	—	—	—
Dec 9	📽️ Project Presentations II 📽️
Dec 10	🎉 Last Day of Classes 🎉	🔬 Projects Due 🔬