Basic concepts

• Supervised Learning, Disciminative Algorithms [pdf]
• Problem Set 0 [pdf]

Supersived learning

• Supervised learning setup. LMS. [pdf]
• Discussion Section: Linear Algebra [Notes]
• Logistic regression. Perceptron. Exponential family [pdf]
• Problem Set 1 [pdf]
• Discussion Section: Probability [pdf][Slides]
• Generative learning algorithms. Gaussian discriminant analysis. Navie Bayes [pdf]
• Support vector machines [pdf]
• Discussion Section: Vectorization [Slides][KNN][Logistic Regression][Softmax Regression][Images][Labels]

Practice ML advice

• Bias/variance tradeoff and error analysis [pdf]
• Learning Theory [pdf]
• Regularization and Model Selection [pdf]
• Online Learning and the Perceptron Algorithm. (opitonal reading) [pdf]
• Advice on applying machine learning [pdf]
• Problem Set 2 [pdf]
• Discussion Section: Convex Optimization Part I [pdf] Part II [pdf]

Deep Learning

• NN architecture. Vectorization. Forward/Back propagation [pdf]
• Additional notes on backpropagation [pdf]
• Discussion Section: Evaluation Metrics [Slides]

Unsupervised Learning

• Unsupervied Learning, K-means clustering. [pdf]
• Mixture of Gaussians [pdf]
• The EM Algorithm [pdf]
• Factor Analysis [pdf]
• Principal Components Analysis [pdf]
• Independent Components Analysis [pdf]
• Problem Set 3 [pdf]

Reinforcement learning and control

• Reinforcement Learning and Control [pdf]
• LQR, DDP and LQG [pdf]
• Problem Set 4 [pdf]
• Generative Adversarial Networks (GANs) [pdf]
• Adversarial examples in ML [pdf]

Supplementary Notes

1. Binary classification with +/-1 labels [pdf]
2. Boosting algorithms and weak learning [pdf]
3. Functional after implementing stump_booster.m in PS2. [here]
4. The representer theorem [pdf]
5. Hoeffding's inequality [pdf]