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 – Spark Project Pitch Day Pitch Deck
Week 2
Sep 9 03 – Class Survey Results, Intro to Pandas Survey Results (1), Pandas
(1) BU email address required
Sep 11 04 – DS Project Lifecycle, Intro to SciKit-Learn DS PLC, SciKit-Learn
Week 3
Sep 16 05 – Probability Refresher Probability
Sep 18 06 – Linear Algebra Refresher Linear Algebra
Week 4
Sep 23 07 – Distance, Similarity Functions and Time Series Distances, Time Series
Sep 25 08 – Clustering I: k-means k-means
Week 5
Sep 30 09 – Clusterin II: In Practice Clustering in Practice
Oct 2 10 – Clustering III: Hierarchical Hierarchical Clustering
Week 6
Oct 7 11 – Clustering IV: GMM and Expectation Maximization GMM and EM
Oct 9 12 – Classification I: Decision Trees and Random Forests Decision Trees
13 – Classification II: k-Nearest Neighbors k-NN
14 – Classification III: Naive Bayes and SVM Naive Bayes and SVM
Week 7
Oct 14 🍂 No Class – Monday Schedule 🍂
Oct 16 15 – SVD Low Rank Approximations SVD
Week 8
Oct 21 16 – Dimensionality Reduction: PCA and t-SNE PCA and t-SNE
Oct 23 17 – Linear Regression Linear Regression
Week 9
Oct 28 18 – Logistic Regression and Regularization Logistic Regression
Oct 30 19 – Neural Networks I: Gradient Descent NN I
Week 10
Nov 4 20 – Neural Networks II: Backpropagation NN II
Nov 6 21 – Neural Networks III: CNNs NN III
Week 11
Nov 11 22 – Recommender Systems Recommender Systems
Nov 13 23 – Graphs I Graphs I
Week 12
Nov 18 24 – Graphs II Graphs II
Nov 20 25 – Time Series Analysis Time Series
Week 13
Nov 25 26 – Intro to NLP NLP
Nov 27 🦃 No Class – Thanksgiving Recess 🌽
Week 14
Dec 2 27 – Lecture to be announced
Dec 4 📽️ Project Presentation I 📽️
Week 15
Dec 9 📽️ Project Presentations II 📽️
Dec 10 🎉 Last Day of Classes 🎉 🔬 Projects Due 🔬
Dec 11 ✨ Spark Demo Day 4pm – 7pm ⚡️
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