Policy Iteration

Supervised learning

Regularization (mathematics)

Unsupervised learning

Latent Factor Models

Feature (machine learning)

Linear classification

Hierarchical Clustering

Reinforcement learning (RL)

Linear regression

Cross-validation (statistics)

Planning

K-means clustering

Basis Functions

Kernel-based Classification

Principal Component Analysis

Markov Decision Process (MDP)

Overfitting

Neural network

Singular Value Decomposition (SVD)

Value iteration

COS 324: Introduction to Machine Learning Princeton University Spring 2019 This introductory course focuses on machine learning, probabilistic reasoning, and decision-making in uncertain environments. A blend of theory and practice, the course aims to answer how systems can learn from experience and manage real-world uncertainties.  This course provides a broad introduction to machine learning, probabilistic reasoning and decision making in uncertain environments. The course should be of interest to undergraduate students in computer science, applied mathematics, sciences and engineering, and lower-level graduate students looking to gain an introduction to the tools of machine learning and probabilistic reasoning with applications to data-intensive problems in the applied sciences, natural sciences and social sciences.

For students with interests in the fundamentals of machine learning and probabilistic artificial intelligence, this course will address three central, related questions in the design and engineering of intelligent systems. How can a system process its perceptual inputs in order to obtain a reasonable picture of the world? How can we build programs that learn from experience? How can we design systems to deal with the inherent uncertainty in the real world?

Our approach to these questions will be both theoretical and practical. We will develop a mathematical underpinning for the methods of machine learning and probabilistic reasoning. We will look at a variety of successful algorithms and applications. We will also discuss the motivations behind the algorithms, and the properties that determine whether or not they will work well for a particular task.  Students should be comfortable with writing non-trivial programs in Python. Students should have a background in basic probability theory, and some level of mathematical sophistication, including calculus and linear algebra. There is no required textbook for the course. This course has its own notes that are considered the required reading. Nevertheless, people learn in different ways and seeing the material presented in different formats can be valuable. To that end, additional optional material is linked on the course website and several books provide useful additional reading:

- Kevin Murphy. *Machine Learning: A Probabilistic Perspective*. MIT Press. 2012.
- Christopher M. Bishop. *Pattern Recognition and Machine Learning*. Springer. 2011.
- David J.C. MacKay. *Information Theory, Inference, and Learning Algorithms*. Cambridge University
Press. 2003. Freely available online at http://www.inference.org.uk/itila/book.html.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. *The Elements of Statistical Learning*.
Springer. 2001. Freely available online at http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. *An Introduction to Statistical
Learning*. Springer. 2013. Freely available online at http://www-bcf.usc.edu/~gareth/ISL/
- Richard S. Sutton and Andrew G. Barto. *Reinforcement Learning: An Introduction*. MIT Press. 1998.
Freely available online at http://incompleteideas.net/book/the-book-2nd.html

COS 324: Introduction to Machine Learning

Machine Learning

Contractions

Asynchronous value iteration

Linear–quadratic regulator (LQR)

Differential dynamic programming (DDP)

Quadruped locomotion

Partially observable Markov decision process (POMDP)

Bandits

Separation Principle

Dynamics Modeling

Kalman Filtering

Policy Gradient

Temporal difference (TD) learning

State–action–reward–state–action (SARSA)

TD-Gammon

Reward Shaping

Linear Programming Approach

Inverse reinforcement learning

Model Predictive Control

Simultaneous Localization and Mapping (SLAM)

Linearly-solvable Markov decision problems

Learning to walk

Q-learning

Function approximation

Exploration / Exploitation

Dynamic programming

CS 294-40: Learning for robotics and control UC Berkeley Fall 2008 This advanced course focuses on the applications of machine learning in the robotics and control field. It covers a wide range of topics including Markov Decision Processes, control theories, estimation methodologies, and robotics principles. Recommended for graduate students. This is an advanced course in learning for robotics and control. The goal of this course is to help the audience with their research in learning for robotics and control or related topics. A tentative list of topics includes:

- Markov decision processes: value iteration, policy iteration, linear programming, Q learning, TD, value function approximation, inverse reinforcement learning
- Control: linear quadratic regulator, differential dynamic programming, receding horizon / model predictive control
- Estimation: (extended) Kalman filters, particle filters, SLAM
- Robotics: basic principles of various robots, sensors, microcontrollers
- Exploration/Exploitation: bandits, no-regret, e^3  Familiarity with mathematical proofs, machine learning, artificial intelligence, optimization, probability, algorithms, linear algebra; ability to implement algorithmic ideas in code (C/C++ and matlab).

Graduate students only (consent of instructor required for undergraduate students, please talk to me after first lecture and hand me summary of relevant classes/experience so I can decide whether to make an exception). 

CS 294-40: Learning for robotics and control

Robotics