CS229 : Machine Learning - 2020

The flagship "ML" course at Stanford , or to say the most popular Machine Learning course worldwide is CS229. CS229 is Math Heavy and is 🔥, unlike the simplified online version at Coursera, "Machine Learning". I completed the online version as a Freshaman and here I take the CS229 Stanford version. CS229 lectures are now available online as a YouTube playlist CS 229 : Autumn 2018. All in all, we have the videos, slides, notes from the course website to learn the content. Stay truthful, maintain Honor Code and Keep Learning. Learning is a journey!

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There's no official textbook , but Pattern Classification - Richard Duda, Peter Hart and David Stork | handout | Syllabus

𓄆 Important Books :
𓊖 Hands on Machine Learning with Scikit Learn and TensorFlow
𓊖 Introduction to Machine Learning - Ethem Alpaydın
𓊖 Machine Learning A Probabilistic Perspective
𓊖 Optimization for Machine Learning
𓊖 Pattern Recognition and Machine Learning - Bishop
𓊖 Pattern Recognition and Machine Learning Solution

Homework (40%) + Mid-term (20%) + Final Project (40%)

print("CS 229") #Machine Learning Journey starts here

🥤 Homeworks (Problem Sets): Solutions

🛦 PSET 0
🛦 PSET 1 : inst - testdata
🛦 PSET 2 : inst
🛦 PSET 3 : inst - peppers_numpy
🛦 PSET 4 : inst - autograder&solution
also check @waynemystir

Course:

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

♞ INTRODUCTION

𓀽 Basic concepts.

♞ SUPERVISED LEARNING

𓀽 Supervised learning setup. LMS. Supervised Learning, Discriminative Algorithms
𓀽 Logistic regression. Perceptron. Exponential family. Linear Algebra
𓀽 Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
𓀽 Support Vector Machines
𓀽 Model selection and feature selection, Probability
𓀽 Evaluating and debugging learning algorithms, Python

♞ LEARNING THEORY

𓀽 Advice on applying machine learning, Bias/variance tradeoff and error analysis
𓀽 Regularization and Model Selection
𓀽 Practical advice on how to use learning algorithms.

♞ DEEP LEARNING

𓀽 Neural Networks, Deep Learning, Backpropagation, Trees
𓀽 Online Learning and the Perceptron Algorithm, Vectorization.

♞ UNSUPERVISED LEARNING

𓀽 Clustering. Unsupervised Learning, k-means clustering
𓀽 EM Algorithm. Mixture of Gaussians
𓀽 Factor analysis
𓀽 PCA (Principal components analysis)
𓀽 ICA (Independent components analysis)

♞ REINFORCEMENT LEARNING AND CONTROL

𓀽 MDPs. Bellman equations, Reinforcement Learning and Control
𓀽 Value iteration and policy iteration. Linear quadratic regulation (LQR), LQR, DDP and LQG
𓀽 Q-learning. Value function approximation. Policy search. Reinforce. POMDPs.
𓀽 On critiques of Machine Learning

Supplementary Notes

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

Section Notes

♚ Linear Algebra Review and Reference pdf
♚ Probability Theory Review pdf
♚ Convex Optimization Overview, Part I pdf
♚ Convex Optimization Overview, Part II pdf
♚ Hidden Markov Models pdf
♚ The Multivariate Gaussian Distribution pdf
♚ More on Gaussian Distribution pdf
♚ Gaussian Processes pdf

Exams

Exams - ⛷ 2019 Autumn Midterm, ⛷ 2019 Spring Midterm, ⛷ 2018 Autumn, ⛷ 2017_Autumn : Solution, ⛷ 2016 Spring : Solution, ⛷ 2016 Autumn : Solution, ⛷ 2015 Autumn : Solution, ⛷ 2014 Autumn : Solution, ⛷ MReview | (Collected from public resources)

⚷ My Solutions for CS229 Exams

✿ MIT ML | ML Cheatsheet | CS229 Cheatsheet | Google Scholar | arXiv | UCI ML dataset repository | CS229#Notes

✿ Official CS229 Notes | CS229 Notes by Shervine | Machine learning study guides tailored to CS 229 | Derivatives Backpropagation and Vectorization | CS229 More Notes - Section Notes - Supplementary Notes | CS229@2008 - syllabus | Statistical Machine Learning CMU Spring 2016 | MIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015 | Advanced Introduction to Machine Learning - CMU - Fall 2015

✿ Cheatsheets :
𓀯 Supervised Learning 𓀯 Unsupervised Learning 𓀯 Machine Learning 𓀯 Deep Learning 𓀯 Algebra Calculus 𓀯 Probability Statistics 𓀯 Combined 𓀯 Machine Learning 𓀯 ML tools

FINAL PROJECT | Past Projects

CS229 gives a lot of importance to Final Project, going through the past projects , I was happy to see the dynamic range of wonderful ideas and application of ML all the way (I have the list - e.g, eg2). Good CS229 projects are either publishable or minor changes to be able to publish the project. NIPS (NeurIPS) , ICML are the ML conferences to show ML works to other people around the world. Here's the CS229 guide to write the final report.