This is a list of the topics that we will tentatively be discussing each day of the semester. I will add links from each one to the slides that I use during the lecture. Those should be present the night before the lecture at the latest. Also listed are the readings/videos for each day and what is due on those days.

8-23Course Introduction
8-28Scala and Collection Refresher plus sbt and plottingFull Playlist 
8-30Parallel Collections (Problems)
9-4Machine Learning and Supervised LearningCh. 1 & 2Data Set Report #1
9-6Spark, Distributed Programming, and RDDs (Problems)
Scala Collection Problems
9-11Probability and Bayesian Decision TheoryAppendix A & Ch. 3
9-13Bayesian Decision Theory

9-18Special RDDs (Problems)
RDD Problems
9-20Moshe Vardi Talk (Chapman Auditorium)

9-25Parametric MethodsCh. 4Quiz #1 (Ch 1-3, A)
9-27SQL Refresher (Problems)
Special RDD Problems
10-2Multivariate MethodsCh. 5
10-4Spark SQL Basics and DataFrames (Problems)
Quiz #2 (Ch. 4-5)
10-8Dimensionality ReductionCh. 6SQL Problems

10-16Datasets and UDFs (Problems)
Spark SQL Problems
10-18ClusteringCh. 7
10-23Clustering and MLLib Introduction/Regression Analysis

10-25MLLib Introduction/Regression Analysis (Problems)
Dataset and UDF Problems
10-30Non-parametric MethodsCh. 8CustomML Step 1
11-1Clustering in Spark (Problems)
Final Project Ideas
11-6Decision TreesCh. 9MLlib Regression
11-8Classification (Problems)

11-13Neural Nets and PerceptronsCh. 11
11-15Multilayer Perceptrons
CustomML Step 2
11-20Thanksgiving (No Class)   
11-22Thanksgiving (No Class)

11-27Training MLPs and Deep Learning
11-29Finish MLPs and Recommendations
CustomML Step 3
Data Set Report #2
12-12, 8:30Final Project Presentations