【V8408】斯坦福大学吴恩达Andrew Ng机器学习教程(热推)

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斯坦福大学吴恩达Andrew Ng机器学习教程
课程概述
机器学习是一门让计算机在非精确编程下進行活动的科学。在过去十年,机器学习促成了无人驾驶车、高效语音识别、精确网络搜索及人类基因组认知的大力发展。机器学习如此无孔不入,你可能已经在不知情的情况下利用过无数次。许多研究者认为,这种手段是达到人类水平AI的最佳方式。这门课程中,你将学习到高效的机器学习技巧,及学会如何利用它为你服务。重点是,你不仅能学到理论基础,更能知晓如何快速有效应用这些技巧到新的问题上。最后,你会接触到硅谷创新中几个优秀的涉及机器学习与AI的应用实例。
 
此课程将广泛介绍机器学习、数据挖掘与统计模式识别的知识。
 
主题包括:
 
(i) 监督学习(参数/非参数算法、支持向量机、内核、神经网络)。
(ii) 非监督学习(聚类、降维、推荐系统、深度学习)。
(iii) 机器学习的优秀案例(偏差/方差理论;机器学习和人工智能的创新过程)课程将拮取案例研究与应用,学习如何将学习算法应用到智能机器人(观感,控制)、文字理解(网页搜索,防垃圾邮件)、计算机视觉、医学信息学、音频、数据挖掘及其他领域上。
 
【课程内容】
1 - 1 - Welcome (7 min)
1 - 2 - What is Machine Learning- (7 min)
1 - 3 - Supervised Learning (12 min)
1 - 4 - Unsupervised Learning (14 min)
2 - 1 - Model Representation (8 min)
2 - 2 - Cost Function (8 min)
2 - 3 - Cost Function - Intuition I (11 min)
2 - 4 - Cost Function - Intuition II (9 min)
2 - 5 - Gradient Descent (11 min)
2 - 6 - Gradient Descent Intuition (12 min)
2 - 7 - Gradient Descent For Linear Regression (10 min)
2 - 8 - What-s Next (6 min)
3 - 1 - Matrices and Vectors (9 min)
3 - 2 - Addition and Scalar Multiplication (7 min)
3 - 3 - Matrix Vector Multiplication (14 min)
3 - 4 - Matrix Matrix Multiplication (11 min)
3 - 5 - Matrix Multiplication Properties (9 min)
3 - 6 - Inverse and Transpose (11 min)
4 - 1 - Multiple Features (8 min)
4 - 2 - Gradient Descent for Multiple Variables (5 min)
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min)
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min)
4 - 5 - Features and Polynomial Regression (8 min)
4 - 6 - Normal Equation (16 min)
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min)
5 - 1 - Basic Operations (14 min)
5 - 2 - Moving Data Around (16 min)
5 - 3 - Computing on Data (13 min)
5 - 4 - Plotting Data (10 min)
5 - 5 - Control Statements- for, while, if statements (13 min)
5 - 6 - Vectorization (14 min)
5 - 7 - Working on and Submitting Programming Exercises (4 min)
6 - 1 - Classification (8 min)
6 - 2 - Hypothesis Representation (7 min)
6 - 3 - Decision Boundary (15 min)
6 - 4 - Cost Function (11 min)
6 - 5 - Simplified Cost Function and Gradient Descent (10 min)
6 - 6 - Advanced Optimization (14 min)
6 - 7 - Multiclass Classification- One-vs-all (6 min)
7 - 1 - The Problem of Overfitting (10 min)
7 - 2 - Cost Function (10 min)
7 - 3 - Regularized Linear Regression (11 min)
7 - 4 - Regularized Logistic Regression (9 min)
8 - 1 - Non-linear Hypotheses (10 min)
8 - 2 - Neurons and the Brain (8 min)
8 - 3 - Model Representation I (12 min)
8 - 4 - Model Representation II (12 min)
8 - 5 - Examples and Intuitions I (7 min)
8 - 6 - Examples and Intuitions II (10 min)
8 - 7 - Multiclass Classification (4 min)
9 - 1 - Cost Function (7 min)
9 - 2 - Backpropagation Algorithm (12 min)
9 - 3 - Backpropagation Intuition (13 min)
9 - 4 - Implementation Note- Unrolling Parameters (8 min)
9 - 5 - Gradient Checking (12 min)
9 - 6 - Random Initialization (7 min)
9 - 7 - Putting It Together (14 min)
9 - 8 - Autonomous Driving (7 min)
10 - 1 - Deciding What to Try Next (6 min)
10 - 2 - Evaluating a Hypothesis (8 min)
10 - 3 - Model Selection and Train-Validation-Test Sets (12 min)
10 - 4 - Diagnosing Bias vs. Variance (8 min)
10 - 5 - Regularization and Bias-Variance (11 min)
10 - 6 - Learning Curves (12 min)
10 - 7 - Deciding What to Do Next Revisited (7 min)
11 - 1 - Prioritizing What to Work On (10 min)
11 - 2 - Error Analysis (13 min)
11 - 3 - Error Metrics for Skewed Classes (12 min)
11 - 4 - Trading Off Precision and Recall (14 min)
11 - 5 - Data For Machine Learning (11 min)
12 - 1 - Optimization Objective (15 min)
12 - 2 - Large Margin Intuition (11 min)
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min)
12 - 4 - Kernels I (16 min)
12 - 5 - Kernels II (16 min)
12 - 6 - Using An SVM (21 min)
13 - 1 - Unsupervised Learning- Introduction (3 min)
13 - 2 - K-Means Algorithm (13 min)
13 - 3 - Optimization Objective (7 min)
13 - 4 - Random Initialization (8 min)
13 - 5 - Choosing the Number of Clusters (8 min)
14 - 1 - Motivation I- Data Compression (10 min)
14 - 2 - Motivation II- Visualization (6 min)
14 - 3 - Principal Component Analysis Problem Formulation (9 min)
14 - 4 - Principal Component Analysis Algorithm (15 min)
14 - 5 - Choosing the Number of Principal Components (11 min)
14 - 6 - Reconstruction from Compressed Representation (4 min)
14 - 7 - Advice for Applying PCA (13 min)
15 - 1 - Problem Motivation (8 min)
15 - 2 - Gaussian Distribution (10 min)
15 - 3 - Algorithm (12 min)
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min)
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min)
15 - 6 - Choosing What Features to Use (12 min)
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min)
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min)
16 - 1 - Problem Formulation (8 min)
16 - 2 - Content Based Recommendations (15 min)
16 - 3 - Collaborative Filtering (10 min)
16 - 4 - Collaborative Filtering Algorithm (9 min)
16 - 5 - Vectorization- Low Rank Matrix Factorization (8 min)
16 - 6 - Implementational Detail- Mean Normalization (9 min)
17 - 1 - Learning With Large Datasets (6 min)
17 - 2 - Stochastic Gradient Descent (13 min)
17 - 3 - Mini-Batch Gradient Descent (6 min)
17 - 4 - Stochastic Gradient Descent Convergence (12 min)
17 - 5 - Online Learning (13 min)
17 - 6 - Map Reduce and Data Parallelism (14 min)
18 - 1 - Problem Description and Pipeline (7 min)
18 - 2 - Sliding Windows (15 min)
18 - 3 - Getting Lots of Data and Artificial Data (16 min)
18 - 4 - Ceiling Analysis- What Part of the Pipeline to Work on Next (14 min)
19 - 1 - Summary and Thank You (5 min)

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