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