VR SCHOOL
ONLINE
Nexus Home
Course Catalog
Meta-Campus
Status
Enter The Nexus
Home
Courses
Part 4: Learning from Smart Algorithms
Curriculum
6 Sections
22 Lessons
10 Weeks
Expand all sections
Collapse all sections
Chapter 10: Starting with Simple Learners
3
1.1
Discovering the Incredible Perceptron
10 mins
1.2
Growing Greedy Classification Trees
10 mins
1.3
Taking a Probabilistic Turn
10 mins
Chapter 11: Leveraging Similarity
4
2.1
Measuring Similarity
10 mins
2.2
Using Distances to Locate Clusters
10 mins
2.3
Tuning the K-Means Algorithm
10 mins
2.4
Finding Similarity by K-Nearest Neighbors
10 mins
Chapter 12: Working with Linear Models the Easy Way
5
3.1
Starting to Combine Features
10 mins
3.2
Mixing Features of Different Types
10 mins
3.3
Switching to Probabilities
10 mins
3.4
Guessing the Right Features
10 mins
3.5
Learning One Example at a Time
10 mins
Chapter 13: Going Beyond the Basics with Support Vector Machines
3
4.1
Revisiting the Separation Problem
10 mins
4.2
Explaining the Algorithm
10 mins
4.3
Classifying and Estimating with SVM
10 mins
Chapter 14: Tackling Complexity with Neural Networks
3
5.1
Revising the Perceptron
10 mins
5.2
Understanding Network Learning and Overfitting
10 mins
5.3
Introducing Deep Learning
10 mins
Chapter 15: Resorting to Ensembles of Learners
4
6.1
Leveraging Decision Trees
10 mins
6.2
Learning from Mistakes and Weak Learners
10 mins
6.3
Boosting via Gradient Descent
10 mins
6.4
Averaging Different Predictors
10 mins
This content is protected, please
login
and
enroll
in the course to view this content!
Modal title
Main Content