MLClass started in October 2011

#HASHTAGUNSET on Twitter,, TwitterStreamGraphs, flickr

Own objectives

  1. application to PIM
  2. pragmatic view of AI

Finished with 100% overall (cf signed letter).

A perfect score but more importantly a clearer view of the field. The score has to be distinguished from AIClass though since here it was possible to know your score and re-submit answers accordingly (within certain limits and timeness). As for AIClass though the main benefit is to enter a lifelong learning process (cf Education#InteractiveClasses).

Attended activities

  • Introduction
  • Linear regression with one variable
  • (Optional) Linear algebra review
  • Linear regression with multiple variables
  • Octave tutorial
  • Logistic Regression
  • One-vs-all Classification
  • Regularization
  • Neural Networks
  • Backpropagation Algorithm
  • Practical advise for applying learning algorithms
  • How to develop and debug learning algorithms
  • Feature and model design, setting up experiments
  • Support Vector Machines (SVMs)
  • Survey of other algorithms: Naive Bayes, Decision Trees, Boosting
  • Unsupervised learning: Agglomerative clustering, k-Means, PCA
  • Combining unsupervised and supervised learning.
  • (Optional) Idependent component analysis
  • Anomaly detection
  • Other applications: Recommender systems. Learning to rank
  • Large-scale/parallel machine learning and big data.
  • Machine learning design / practical methods
  • Team design of machine learning systems

Overall remarks and conclusions

  • meet him
  • could be improved by doing that
  • see also that other event

Back to the Menu

Other reviews or coverage

  • here

See also

To do

  1. see AIClass
  2. can artificial data synthesis used in OCR be considered a minimal form of artificial creativity?