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吴恩达老师 2022年 机器学习 课程

deeplearning.ai 提供

coursera

PART 01: Supervised Machine Learning: Regression and Classification

Week 01 Introduction to Machine Learning

  • Welcome to machine learning
  • Applications of machine learning
  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Jupyter Notebooks
  • Linear regression model
  • Cost function formula
  • Cost function intuition
  • Visualizing the cost function
  • Visualization examples
  • Gradient descent
  • Implementing gradient descent
  • Gradient descent for linear regression
  • Running gradient descent

Practice quiz: Supervised vs unsupervised learning Practice quiz: Regression Practice quiz: Train the model with gradient descent

Week 02 Regression with multiple input variables

  • Multiple features
  • Vectorization
  • Gradient descent for multiple linear regression
  • Feature scaling
  • Checking gradient descent for convergence
  • Choosing the learning rate
  • Feature engineering
  • Polynomial regression

Practice quiz: Multiple linear regression Practice quiz: Gradient descent in practice

Week 03 Classification

  • Motivations
  • Logistic regression
  • Decision boundary
  • Cost function for logistic regression
  • Simplified Cost Function for Logistic Regression
  • Gradient Descent Implementation
  • The problem of overfitting
  • Addressing overfitting
  • Cost function with regularization
  • Regularized linear regression
  • Regularized logistic regression
  • Andrew Ng and Fei-Fei Li on Human-Centered AI

Practice quiz: Classification with logistic regression Practice quiz: Cost function for logistic regression Practice quiz: Gradient descent for logistic regression Practice quiz: The problem of overfitting

PART 02: Advanced Learning Algorithms

Week 04 Neural Networks

  • Welcome!
  • Neurons and the brain
  • Demand Prediction
  • Example: Recognizing Images
  • Neural network layer
  • More complex neural networks
  • Inference: making predictions (forward propagation)
  • Inference in Code
  • Data in TensorFlow
  • Building a neural network
  • Forward prop in a single layer
  • General implementation of forward propagation
  • Is there a path to AGI?
  • How neural networks are implemented efficiently
  • Matrix multiplication
  • Matrix multiplication rules
  • Matrix multiplication code

Practice quiz: Neural networks intuition Practice quiz: Neural network model Practice quiz: TensorFlow implementation Practice quiz: Neural network implementation in Python

Week 05 Neural network training

  • TensorFlow implementation
  • Training Details
  • Alternatives to the sigmoid activation
  • Choosing activation functions
  • Why do we need activation functions?
  • Multiclass
  • Softmax
  • Neural Network with Softmax output
  • Improved implementation of softmax
  • Classification with multiple outputs
  • Advanced Optimization
  • Additional Layer Types
  • What is a derivative?
  • Computation graph
  • Larger neural network example

Practice quiz: Neural Network Training Practice quiz: Activation Functions Practice quiz: Multiclass Classification Practice quiz: Additional Neural Network Concepts

Week 06 Advice for applying machine learning

  • Deciding what to try next
  • Evaluating a model
  • Model selection and training/cross validation/test sets
  • Diagnosing bias and variance
  • Regularization and bias/variance
  • Establishing a baseline level of performance
  • Learning curves
  • Deciding what to try next revisited
  • Bias/variance and neural networks
  • Iterative loop of ML development
  • Error analysis
  • Adding data
  • Transfer learning: using data from a different task
  • Full cycle of a machine learning project
  • Fairness, bias, and ethics
  • Error metrics for skewed datasets
  • Trading off precision and recall

Practice quiz: Advice for applying machine learning Practice quiz: Bias and variance Practice quiz: Machine learning development process

Week 07 Decision trees

  • Decision tree model
  • Learning Process
  • Measuring purity
  • Choosing a split: Information Gain
  • Putting it together
  • Using one-hot encoding of categorical features
  • Continuous valued features
  • Regression Trees
  • Using multiple decision trees
  • Sampling with replacement
  • Random forest algorithm
  • XGBoost
  • When to use decision trees
  • Andrew Ng and Chris Manning on Natural Language Processing

Practice quiz: Decision trees Practice quiz: Decision tree learning Practice quiz: Tree ensembles

PART 03: Unsupervised Learning, Recommenders, Reinforcement Learning

Week 08 Unsupervised learning

  • Welcome!
  • What is clustering?
  • K-means intuition
  • K-means algorithm
  • Optimization objective
  • Initializing K-means
  • Choosing the number of clusters
  • Finding unusual events
  • Gaussian (normal) distribution
  • Anomaly detection algorithm
  • Developing and evaluating an anomaly detection system
  • Anomaly detection vs. supervised learning
  • Choosing what features to use

Practice quiz: Clustering Practice quiz: Anomaly detection

Week 09 Recommender systems

  • Making recommendations
  • Using per-item features
  • Collaborative filtering algorithm
  • Binary labels: favs, likes and clicks
  • Mean normalization
  • TensorFlow implementation of collaborative filtering
  • Finding related items
  • Collaborative filtering vs Content-based filtering
  • Deep learning for content-based filtering
  • Recommending from a large catalogue
  • Ethical use of recommender systems
  • TensorFlow implementation of content-based filtering
  • Reducing the number of features
  • PCA algorithm
  • PCA in code

Practice quiz: Collaborative Filtering Practice quiz: Recommender systems implementation Practice quiz: Content-based filtering

Week 10 Reinforcement learning

  • What is Reinforcement Learning?
  • Mars rover example
  • The Return in reinforcement learning
  • Making decisions: Policies in reinforcement learning
  • Review of key concepts
  • State-action value function definition
  • State-action value function example
  • Bellman Equation
  • Random (stochastic) environment
  • Example of continuous state space applications
  • Lunar lander
  • Learning the state-value function
  • Algorithm refinement: Improved neural network architecture
  • Algorithm refinement: ϵ-greedy policy
  • Algorithm refinement: Mini-batch and soft updates
  • The state of reinforcement learning
  • Summary and thank you
  • Andrew Ng and Chelsea Finn on AI and Robotics

Practice quiz: Reinforcement learning introduction Practice quiz: State-action value function Practice quiz: Continuous state spaces

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