斯坦福大學(xué)英文版機(jī)器學(xué)習(xí)視頻教程下載

 

 

 

 

課程介紹：

 

機(jī)器學(xué)習(xí)是一門讓計算機(jī)在非精確編程下進(jìn)行活動的科學(xué)。在過去十年，機(jī)器學(xué)習(xí)促成了無人駕駛車、高效語音識別、精確網(wǎng)絡(luò)搜索及人類基因組認(rèn)知的大力發(fā)展。機(jī)器學(xué)習(xí)如此無孔不入，你可能已經(jīng)在不知情的情況下利用過無數(shù)次。許多研究者認(rèn)為，這種手段是達(dá)到人類水平AI的最佳方式。這門課程中，你將學(xué)習(xí)到高效的機(jī)器學(xué)習(xí)技巧，及學(xué)會如何利用它為你服務(wù)。重點是，你不僅能學(xué)到理論基礎(chǔ)，更能知曉如何快速有效應(yīng)用這些技巧到新的問題上。最后，你會接觸到硅谷創(chuàng)新中幾個優(yōu)秀的涉及機(jī)器學(xué)習(xí)與AI的應(yīng)用實例。此套英文版機(jī)器學(xué)習(xí)視頻教程包括視頻和源碼，需要看英文版的朋友可以學(xué)學(xué)。

 

 

斯坦福大學(xué)英文版機(jī)器學(xué)習(xí)視頻教程目錄介紹：

1 - 1 - Welcome (7 min).mkv
1 - 2 - What is Machine Learning_ (7 min).mkv
1 - 3 - Supervised Learning (12 min).mkv
1 - 4 - Unsupervised Learning (14 min).mkv
2 - 1 - Model Representation (8 min).mkv
2 - 2 - Cost Function (8 min).mkv
2 - 3 - Cost Function - Intuition I (11 min).mkv
2 - 4 - Cost Function - Intuition II (9 min).mkv
2 - 5 - Gradient Descent (11 min).mkv
2 - 6 - Gradient Descent Intuition (12 min).mkv
2 - 7 - GradientDescentForLinearRegression  (6 min).mkv
2 - 8 - What_'s Next (6 min).mkv
3 - 1 - Matrices and Vectors (9 min).mkv
3 - 2 - Addition and Scalar Multiplication (7 min).mkv
3 - 3 - Matrix Vector Multiplication (14 min).mkv
3 - 4 - Matrix Matrix Multiplication (11 min).mkv
3 - 5 - Matrix Multiplication Properties (9 min).mkv
3 - 6 - Inverse and Transpose (11 min).mkv
4 - 1 - Multiple Features (8 min).mkv
4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv
4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv
4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv
4 - 5 - Features and Polynomial Regression (8 min).mkv
4 - 6 - Normal Equation (16 min).mkv
4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv
5 - 1 - Basic Operations (14 min).mkv
5 - 2 - Moving Data Around (16 min).mkv
5 - 3 - Computing on Data (13 min).mkv
5 - 4 - Plotting Data (10 min).mkv
5 - 5 - Control Statements_ for, while, if statements (13 min).mkv
5 - 6 - Vectorization (14 min).mkv
5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv
6 - 1 - Classification (8 min).mkv
6 - 2 - Hypothesis Representation (7 min).mkv
6 - 3 - Decision Boundary (15 min).mkv
6 - 4 - Cost Function (11 min).mkv
6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv
6 - 6 - Advanced Optimization (14 min).mkv
6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv
7 - 1 - The Problem of Overfitting (10 min).mkv
7 - 2 - Cost Function (10 min).mkv
7 - 3 - Regularized Linear Regression (11 min).mkv
7 - 4 - Regularized Logistic Regression (9 min).mkv
8 - 1 - Non-linear Hypotheses (10 min).mkv
8 - 2 - Neurons and the Brain (8 min).mkv
8 - 3 - Model Representation I (12 min).mkv
8 - 4 - Model Representation II (12 min).mkv
8 - 5 - Examples and Intuitions I (7 min).mkv
8 - 6 - Examples and Intuitions II (10 min).mkv
8 - 7 - Multiclass Classification (4 min).mkv
9 - 1 - Cost Function (7 min).mkv
9 - 2 - Backpropagation Algorithm (12 min).mkv
9 - 3 - Backpropagation Intuition (13 min).mkv
9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv
9 - 5 - Gradient Checking (12 min).mkv
9 - 6 - Random Initialization (7 min).mkv
9 - 7 - Putting It Together (14 min).mkv
9 - 8 - Autonomous Driving (7 min).mkv
10 - 1 - Deciding What to Try Next (6 min).mkv
10 - 2 - Evaluating a Hypothesis (8 min).mkv
10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv
10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv
10 - 5 - Regularization and Bias_Variance (11 min).mkv
10 - 6 - Learning Curves (12 min).mkv
10 - 7 - Deciding What to Do Next Revisited (7 min).mkv
11 - 1 - Prioritizing What to Work On (10 min).mkv
11 - 2 - Error Analysis (13 min).mkv
11 - 3 - Error Metrics for Skewed Classes (12 min).mkv
11 - 4 - Trading Off Precision and Recall (14 min).mkv
11 - 5 - Data For Machine Learning (11 min).mkv
12 - 1 - Optimization Objective (15 min).mkv
12 - 2 - Large Margin Intuition (11 min).mkv
12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv
12 - 4 - Kernels I (16 min).mkv
12 - 5 - Kernels II (16 min).mkv
12 - 6 - Using An SVM (21 min).mkv
13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv
13 - 2 - K-Means Algorithm (13 min).mkv
13 - 3 - Optimization Objective (7 min)(1).mkv
13 - 3 - Optimization Objective (7 min).mkv
13 - 4 - Random Initialization (8 min).mkv
13 - 5 - Choosing the Number of Clusters (8 min).mkv
14 - 1 - Motivation I_ Data Compression (10 min).mkv
14 - 2 - Motivation II_ Visualization (6 min).mkv
14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv
14 - 4 - Principal Component Analysis Algorithm (15 min).mkv
14 - 5 - Choosing the Number of Principal Components (11 min).mkv
14 - 6 - Reconstruction from Compressed Representation (4 min).mkv
14 - 7 - Advice for Applying PCA (13 min).mkv
15 - 1 - Problem Motivation (8 min).mkv
15 - 2 - Gaussian Distribution (10 min).mkv
15 - 3 - Algorithm (12 min).mkv
15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv
15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv
15 - 6 - Choosing What Features to Use (12 min).mkv
15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv
15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv
16 - 1 - Problem Formulation (8 min).mkv
16 - 2 - Content Based Recommendations (15 min).mkv
16 - 3 - Collaborative Filtering (10 min).mkv
16 - 4 - Collaborative Filtering Algorithm (9 min).mkv
16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv
16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv
17 - 1 - Learning With Large Datasets (6 min).mkv
17 - 2 - Stochastic Gradient Descent (13 min).mkv
17 - 3 - Mini-Batch Gradient Descent (6 min).mkv
17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv
17 - 5 - Online Learning (13 min).mkv
17 - 6 - Map Reduce and Data Parallelism (14 min).mkv
18 - 1 - Problem Description and Pipeline (7 min).mkv
18 - 2 - Sliding Windows (15 min).mkv
18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv
18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv
19 - 1 - Summary and Thank You (5 min).mkv
pdf
ppt
中英文字幕.rar
如何添加中文字幕.doc
教程和筆記
機(jī)器學(xué)習(xí)課程源代碼

 

 

斯坦福大學(xué)英文版機(jī)器學(xué)習(xí)視頻教程部分資料截圖展示：