Sample Complexity

Decision Tree Learning

Margins

Naive Bayes

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

Neural network

Convolutional neural network (CNN)

Objective-Based Clustering

Principal Component Analysis (PCA)

Semi-Supervised Learning

Learning Linear Separators

Maximum Likelihood Estimation (MLE)

Bag of Words

Computer Vision

Generalization and Overfitting

Model Selection and Regularization

Support Vector Machine (SVM)

Markov Decision Process (MDP)

Minimizing Squared Error

Deep Networks

Adaboost

Learning Representations

Active Learning

Co-training

Q-learning

Overfitting

Conditional Independence

Gradient descent

Kernelizing Perceptron

Kernelizing SVM

VC Dimension Based Bounds

Linear regression

Backpropagation

Boosting

Hierarchical Clustering

Interactive Learning

Transductive SVM

Value iteration

Bayes' theorem

Perceptron

Maximum A Posteriori (MAP)

Logistic Regression

Kernel (operating system)

Geometric Margins

Structural Risk Minimization

Maximizing Data Likelihood

Unsupervised learning

Dimensionality Reduction

Sampling Bias

Reinforcement learning (RL)

Convolution

10-401 Introduction to Machine Learning Carnegie Mellon University Spring 2018 A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.  Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, Support Vector Machines, neural networks, boosting, statistical learning methods, unsupervised learning, active leaerning, and reinforcement learning. Short programming assignments include hands-on experiments with various learning algorithms.   - Machine Learning, Tom Mitchell. (optional)
- Pattern Recognition and Machine Learning, Christopher Bishop. (optional)
- Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, [available online](https://ebookcentral.proquest.com/lib/cm/detail.action?docID=3339490), (optional)

10-401 Introduction to Machine Learning

Machine Learning

Monte Carlo method

Randomized Algorithms

Large Deviation Bounds

Packet Routing

Martingales

Rademacher complexity

Probabilistic Method

Hashing

Markov Chains

CSCI 1550/2450 Probabilistic Methods in Computer Science Brown University Spring 2022 This analytical course dives into the mathematical underpinnings of computing successes like machine learning and cryptography, emphasizing the role of probability, randomness, and statistics. Students will explore mathematical models, theorems, and proofs. Practical implementations are not covered, focusing instead on the theories driving computational probabilities. CSCI 1550/2450 is a course on the mathematics that motivates, formulates, and explains many of the great successes of computing, including statistical machine learning, Monte Carlo methods, and modern cryptography. Probability, randomness, and statistics play a key role in these and almost any other modern computer science application. This course introduces the novel mathematical and computation methods that were developed at the interplay of probability and computing. The course focuses on mathematical models, theorems and proofs, and leaves implementation and experiments to other courses. - CSCI 1550/2540 is a theory/analytical course - analysis, theorems, no implementations.
- The course covers modern mathematics at the interface of probability theory and computation
- Formulates, and explains many of the great successes of computing, such as machine learning, cryptography, modern finance, computational biology, etc.
- This course focuses on tools, not particular applications. CS145 or equivalent (first three chapters in the course textbook). ## Textbook

The textbook for the course is *[Probability and Computing Randomization and Probabilistic Techniques in Algorithms and Data Analysis, 2nd Edition](https://www.amazon.com/Probability-Computing-Randomization-Probabilistic-Techniques/dp/110715488X/ref=dp_ob_title_bk)* by [Michael Mitzenmacher](http://www.eecs.harvard.edu/%7Emichaelm/) and [Eli Upfal](/people/eli).

[Errata for the 1st Printing](http://www.eecs.harvard.edu/%7Emichaelm/MUErrataPrint1.pdf), [Errata for the 2nd Printing](http://www.eecs.harvard.edu/%7Emichaelm/MUErrataPrint2.pdf)

CSCI 1550/2450 Probabilistic Methods in Computer Science

Mathematical Foundations