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AI: Machine Learning Techniques with Scala
Section 1 Welcome
Lecture 1 Welcome (2:01)
Lecture 2 Functional Combinators (4:32)
Lecture 3 Scala Traits, Classes, and Objects (3:42)
Lecture 4 IntelliJ IDEA (1:55)
Lecture 5 The Breeze Library for Linear Algebra (2:53)
Lecture 6 WISP for Plotting (2:07)
Section 2 Exploratory Data Analysis with Scala
Lecture 7 Exploratory Data Analysis (2:46)
Lecture 8 Using DataFrames with Scala and Plotting with Breeze (4:19)
Section 3 Supervised Learning
Lecture 9 Supervised Learning Problem Formulation (2:41)
Lecture 10 Two Basic Regression Algorithms (4:13)
Lecture 11 Implementing Linear Regression and GLMs in Scala (4:25)
Lecture 12 Two Basic Classification Algorithms (4:32)
Lecture 13 Implementing K-Nearest Neighbors and Naive Bayes in Scala (7:26)
Lecture 14 Model Selection (5:15)
Section 4 Unsupervised Learning
Lecture 15 Unsupervised Learning (3:30)
Lecture 16 Implementing K-means Algorithm in Scala (5:27)
Lecture 17 Mixture of Gaussians Clustering (3:56)
Lecture 18 Implementing Mixture of Gaussians Clustering in Scala (5:06)
Lecture 19 Dimensionality Reduction with PCA (3:29)
Lecture 20 Implementing PCA in Scala (3:15)
Section 5 Neural Networks
Lecture 21 Intro to Feed-Forward Neural Networks (5:05)
Lecture 22 Implementing the Feed-Forward Neural Network in Scala (4:54)
Lecture 23 Introduction to Restricted Boltzmann Machines (4:13)
Lecture 24 Implementing Restricted Boltzmann Machines (4:09)
Section 6 Outro
Lecture 25 The Akka Actor Model for Concurrency (4:04)
Lecture 26 A Multi-threaded K-Nearest Neighbors Implementation with Akka (6:37)
Lecture 27 Apache Spark (4:07)
Lecture 28 Running Linear Regression on Spark with MLlib (3:38)
Working Files
Working Files
Lecture 23 Introduction to Restricted Boltzmann Machines
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