MACHINE LEARNING

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MACHINE LEARNING

 

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

In this course, you will learn the foundations of deep learning. When you finish this class, you will: – Understand the major technology trends driving Deep Learning – Be able to build, train and apply fully connected deep neural networks – Know how to implement efficient (vectorized) neural networks – Understand the key parameters in a neural network’s architecture

Prerequisites

  • knowledge of basic computer science principles and skills
  • Familiarity with the basic probability theory.
  • Familiarity with the basic linear algebra

Timing

  • 6 weeks : Python + linear algebra
  • 6 weeks: machine learning
  • 6 weeks: deeplearning

Theory content

  • Introduction
  • Linear Algebra Review
  • Python
  • Linear Regression with Multiple Variables
  • Logistic Regression
  • Neural Networks: Representation
  • Neural Networks: Learning
  • Advice for Applying Machine Learning
  • Support Vector Machines
  • Unsupervised Learning
  • Anomaly Detection
  • Large Scale Machine Learning
  • Applications
  • Algorithmic models of learning.
  • Learning classifiers, functions, relations, grammars.
  • decision trees
  • support vector machines
  • Bayesian networks
  • bag of words classifiers
  • N-gram models
  • Markov and Hidden Markov models
  • probabilistic relational models
  • association rules,
  • feature selection and visualization.
  • k-means clustering
  • Reinforcement learning
  • Learning from heterogeneous, distributed data
  • applications in data mining,
  • pattern recognition.
  • Deeplearning
  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models

practical : Programming Assignment

  • Octave/Matlab Tutorial
  • Python Basics with numpy
  • Logistic Regression with a Neural Network mindset
  • Linear Regression with One Variable
  • Regularization
  • Neural Networks: Representation
  • Neural Networks: Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design
  • Support Vector Machines
  • Unsupervised Learning
  • Principal Component Analysis
  • Anomaly Detection
  • Recommender Systems
  • Large Scale Machine Learning
  • Photo OCR
  • Planar data classification with a hidden layer
  • Building your deep neural network: Step by Step
  • Deep Neural Network Application
  • Regularization
  • Gradient Checking
  • Optimization algorithms
  • Tensorflow
  • Bird recognition in the city of Peacetopia (case study)
  • Autonomous driving (case study)
  • Convolutional Model: step by step
  • Convolutional model: application
  • Keras Tutorial – The Happy House
  • Residual Networks
  • Car detection with YOLOv2
  • Special applications: Face recognition & Neural style transfer
  • Art generation with Neural Style Transfer
  • Dinosaur Island – Character-Level Language Modeling
  • Operations on word vectors – Debiasing
  • Neural Machine Translation with Attention
  • Trigger word detection