機器學習是一門讓計算機在非精確編程下進行活動的科學。在過去十年，機器學習促成了無人駕駛車、高效語音識別、精確網絡搜索及人類基因組認知的大力發展。機器學習如此無孔不入，你可能已經在不知情的情況下利用過無數次。許多研究者認為，這種手段是達到人類水平AI的最佳方式。這門課程中，你將學習到高效的機器學習技巧，及學會如何利用它為你服務。重點是，你不僅能學到理論基礎，更能知曉如何快速有效應用這些技巧到新的問題上。最后，你會接觸到硅谷創新中幾個優秀的涉及機器學習與AI的應用實例。
此課程將廣泛介紹機器學習、數據挖掘與統計模式識別的知識。
主題包括：
(i) 監督學習（參數/非參數算法、支持向量機、內核、神經網絡）。
(iii) 機器學習的優秀案例（偏差/方差理論；機器學習和人工智能的創新過程）課程將拮取案例研究與應用，學習如何將學習算法應用到智能機器人（觀感，控制）、文字理解（網頁搜索，防垃圾郵件）、計算機視覺、醫學信息學、音頻、數據挖掘及其他領域上。
【課程內容】
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)<br style="overflow-wrap: break-word; color: rgb(111, 116, 121); font-family: -apple-system, " helvetica="" neue",="" helvetica,="" arial,="" "pingfang="" sc",="" "hiragino="" sans="" gb",="" stheiti,="" "microsoft="" yahei",="" jhenghei",="" simsun,="" sans-serif;="" font-size:="" 14px;"="">
