Embedded Files
Lectures and reading
Lectures and reading
Files for scribe notes: available in canvas
Files for scribe notes: available in canvas
Lecture 1: Introduction to learning theory, learning from expert advice and the online learning model
Lecture 1: Introduction to learning theory, learning from expert advice and the online learning model
Examples of machine learning problems, classification of learning models, learning from expert advice, the weighted majority + RWM algorithms, the online learning model, definition of regret, multiplicative weights algorithm & analysis
Reading:
Chapter 1 in book 1
Alan Turing's paper on AI: https://www.csee.umbc.edu/courses/471/papers/turing.pdf
Lecture 2: Learning from examples
Lecture 2: Learning from examples
The statistical learning model and PAC learning, defining generalization, ERM and/or online to batch and concluding learnability of finite hypothesis classes, the computational difficulty of learning hyperplanes and motivating convex optimization. Touch upon learnability of infinite hypothesis classes, and generalization theory for them, via online 2 batch and/or compression arguments.
Reading:
Chapter 9 in book 1
Book 2
Lecture 3: Convex optimization, OCO and online gradient descent
Lecture 3: Convex optimization, OCO and online gradient descent
Learning continuous hypothesis classes via convex optimization, NP-hardness of hyperplane learning and convex relaxation with SVM, introduction to convex analysis and mathematical optimization, solution concepts for convex and non-convex optimization, gradient descent and its analysis for both convex and nonconvex optimization, online convex optimization, online gradient descent
Reading:
Chapter 2,3 in book 1
Lecture 4: Non-convex optimization
Lecture 4: Non-convex optimization
Wrap up OCO, the differences between OEG and OGD, Mirror Descent
Non-convex optimization in ML: uses and algorithms
GD and SGD with bounds for first order optimality
Reading:
Chapter 2,3 in book 1
Optimization chapter in new book on Deep Learning by Prof. Sanjeev Arora
Lecture 5: Boosting and ensemble methods
Lecture 5: Boosting and ensemble methods
Weak learning vs. strong learning, boosting of weak learners, AdaBoost and its analysis
Reading:
Chapter 11 in book 1
Book 4
Lecture 6: Learning with limited information: bandits
Lecture 6: Learning with limited information: bandits
The multi-armed bandit problem, explore-exploit trade-off, EXP3 and its analysis, bandit convex optimization, bandit linear optimization, self-concordant regularizers
Reading:
Chapter 6 in book 1
Bandit Algorithms by Lattimore and Szepasvari
Lecture 7: Games, learning, duality and linear programming
Lecture 7: Games, learning, duality and linear programming
Introduction to the theory of games, linear programming, duality, min-max theorem and its proof using regret
Reading:
Chapter 8 in book 1
Lecture 8: Recommender systems, matrix learning and projection-free learning
Lecture 8: Recommender systems, matrix learning and projection-free learning
Recommender systems and matrix completion, the Frank-Wolfe algorithm, online projection free methods
Reading:
Chapter 7 in book 1
Lecture 9: Reinforcement learning
Lecture 9: Reinforcement learning
Learning with state, the MDP model, the Bellman equation and dynamic programming, learning MDPs via value iteration, online learning of MDPs
Reading:
Book 4
Lecture 10: Optimal control and linear dynamical systems
Lecture 10: Optimal control and linear dynamical systems
The problem of control and its relationship to RL, examples of control problems, the optimal control problem, Linear Dynamical Systems, LQR and its solution via Bellman equation
Reading:
Lecture 11: Special research guest lectures
Lecture 11: Special research guest lectures
Lecture 12: Regret minimization in control: Nonstochastic control
Lecture 12: Regret minimization in control: Nonstochastic control
The nonstochastic control problem, Disturbance Action Policies, the Gradient Perturbation Controller
Page updated
Google Sites
Report abuse