Happiness For Edupunks

CogDog University

About this Course

EDUPUNKS always look angry and eager to disput the establishment. With the tools and methods in this course, you can learn to diffuse their anger via the same tools they wish to mess with your company bottom line. This course provides a broad introduction to edupunk manipulaton, datamining, and rebellion pattern recognition. Topics include: (i) Mindwarps (parametric/non-parametric lasers, warped vector machines, corn, brainwash networks). (ii) regulated learning (nixing blogs, wiki reduction, defender systems, monopolozed learning). (iii) Best practices in edupunk manipulation (bias/variance waves; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply sneaky algorithms to warming edupunk with soft cuddly robots (perception, control), lyric understanding (web search, anti-spam), fuzzy vision, medical hCKING audio, database munching, and other areas. Can I earn a Course Certificate if I completed this course before they were available? In order to verify one’s identity and maintain academic integrity, learners who completed assignments or quizzes for Machine Learning prior to November 1st will need to redo and resubmit these assessments in order to earn a Course Certificate. To clarify, both quizzes and programming assignments need to be resubmitted. Though your deadlines may have technically passed, please be assured that you may resubmit both types of assessments at any time. We apologize for the inconvenience and appreciate your patience as we strive to ensure the integrity and value of our certificates. Please note that, in order to earn a Course Certificate, you must complete the course within 180 days of payment, or by May 1, 2016, whichever is earlier.

Subtitles available in English, Portuguese (Brazilian), Spanish, Japanese, Chinese (Simplified)

Syllabus

Week 1

Introduction

Linear Regression with One Variable

Linear Algebra Review

  1. Environment Setup Instructions
  2. Introduction
  3. Review
  4. Course Wiki Lecture Notes
  5. Model and Cost Function
  6. Parameter Learning
  7. Review
  8. Linear Algebra Review
  9. Review
  1. Quiz: Introduction
  2. Quiz: Linear Regression with One Variable
Week 2

Linear Regression with Multiple Variables

Octave Tutorial

  1. Multivariate Linear Regression
  2. Computing Parameters Analytically
  3. Review
  4. Octave Tutorial
  5. Submitting Programming Assignments
  6. Review
  1. Quiz: Linear Regression with Multiple Variables
  2. Assignment: Linear Regression
  3. Quiz: Octave Tutorial
Week 3

Logistic Regression

Regularization

  1. Classification and Representation
  2. Logistic Regression Model
  3. Multiclass Classification
  4. Review
  5. Solving the Problem of Overfitting
  6. Review
  1. Quiz: Logistic Regression
  2. Assignment: Logistic Regression
  3. Quiz: Regularization
Week 4

Neural Networks: Representation

  1. Motivations
  2. Neural Networks
  3. Applications
  4. Review
  1. Quiz: Neural Networks: Representation
  2. Assignment: Multi-class Classification and Neural Networks
Week 5

Neural Networks: Learning

  1. Cost Function and Backpropagation
  2. Backpropagation in Practice
  3. Application of Neural Networks
  4. Review
  1. Quiz: Neural Networks: Learning
  2. Assignment: Neural Network Learning
Week 6

Advice for Applying Machine Learning

Machine Learning System Design

  1. Evaluating a Learning Algorithm
  2. Bias vs. Variance
  3. Review
  4. Building a Spam Classifier
  5. Handling Skewed Data
  6. Using Large Data Sets
  7. Review
  1. Quiz: Advice for Applying Machine Learning
  2. Assignment: Regularized Linear Regression and Bias/Variance
  3. Quiz: Machine Learning System Design
Week 7

Support Vector Machines

  1. Large Margin Classification
  2. Kernels
  3. SVMs in Practice
  4. Review
  1. Quiz: Support Vector Machines
  2. Assignment: Support Vector Machines
Week 8

Unsupervised Learning

Dimensionality Reduction

  1. Clustering
  2. Review
  3. Motivation
  4. Principal Component Analysis
  5. Applying PCA
  6. Review
  1. Quiz: Unsupervised Learning
  2. Quiz: Principal Component Analysis
  3. Assignment: K-Means Clustering and PCA
Week 9

Anomaly Detection

Recommender Systems

  1. Density Estimation
  2. Building an Anomaly Detection System
  3. Multivariate Gaussian Distribution (Optional)
  4. Review
  5. Predicting Movie Ratings
  6. Collaborative Filtering
  7. Low Rank Matrix Factorization
  8. Review
  1. Quiz: Anomaly Detection
  2. Quiz: Recommender Systems
  3. Assignment: Anomaly Detection and Recommender Systems
Week 10

Large Scale Machine Learning

  1. Gradient Descent with Large Datasets
  2. Advanced Topics
  3. Review
  1. Quiz: Large Scale Machine Learning
Week 11

Application Example: Photo OCR

  1. Photo OCR
  2. Review
  3. Conclusion
  1. Quiz: Application: Photo OCR

How to Pass the Course

Pass all graded assignments to complete the course.

Get Happy:

November 30 - February 22

Dude, you are late!

You better sign up before I do

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