46195 - Machine Learning

An Introductory Course On Learning Systems in The Context Of Signal Processing, Artificial Intelligence and Control. Problems of Classification, Regression and Clustering. Neural Networks# Multi-level Perceptrons and Radial Basis Functions. Decision Trees. Elements of The Learning Theory# The Bayesian Approach, Hypothesis Spaces. Dimensionality Reduction Using Principal Components. Classification Using Support Vector Machines. Reinforcement Learning.

Faculty: Electrical and Computer Engineering
|Undergraduate Studies |Graduate Studies

Pre-required courses

(44130 - Signals and Systems and 104034 - Introduction to Probability H) or (44131 - Signals and Systems and 104034 - Introduction to Probability H)

Course with no extra credit

36049 - Neural Networks For Control/diagnostic 236756 - Introduction to Machine Learning

• An introduction to statistical learning : with applications in R
• An introduction to statistical learning : with applications in R - James, Gareth
• An Introduction to Statistical Learning with Applications in R - James, Gareth.
• Introduction to machine learning - Alpaydin, Ethem
• Introduction to machine learning - Alpaydin, Ethem
• Introduction to machine learning - Alpaydin, Ethem,
• Learning from data : a short course - Abu-Mostafa, Yaser S.
• Machine learning - Mitchell, Tom M.
• Pattern classification - Duda, Richard O.
• Pattern recognition and machine learning - Bishop, Christopher M.
• The elements of statistical learning : data mining, inference, and prediction - Hastie, Trevor
• The elements of statistical learning data mining, inference, and prediction - Hastie, Trevor
• The Elements of Statistical Learning Data Mining, Inference, and Prediction - Hastie, Trevor.
• Understanding machine learning : from foundations to algorithms - Shalev-Shwartz, Shai,
• Understanding machine learning : from theory to algorithms - Shalev-Shwartz, Shai