Representations

Connectionist Machines

McCullough and Pitt model

Hebb’s learning rule

Rosenblatt’s perceptron

Universal Approximator

Neural network

Perceptron learning rule

Empirical risk minimization

Optimization

Gradient descent

Back-propagation

Momentum

Nestorov

Convergence

Learning Rates

Optimization Algorithms

RMSProp

Stochastic gradient descent (SGD)

Acceleration

Overfitting

Regularization (mathematics)

Convolutional neural network (CNN)

Translation Invariance

Cascade Correlation Filters

Recurrent neural network (RNN)

Bidirectional RNNs

Sequence Prediction

Long Short-Term Memory (LSTM)

Connectionist Temporal Classification (CTC)

Autoencoders

Hopfield Networks

Boltzmann Machines

Normalizing Flows

Variational autoencoder (VAE)

Generative adversarial network (GAN)

Reinforcement learning (RL)

Multi-layer Perceptron

Sequence-to-sequence (Seq2Seq)

AdaGrad

11-785 Introduction to Deep Learning Carnegie Mellon University Spring 2020 This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required. “Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.

In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.

If you are only interested in the lectures, you can watch them on the YouTube channel listed below.

### Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.  1. We will be using one of several toolkits (the primary toolkit for recitations/instruction is PyTorch). The toolkits are largely programmed in Python. You will need to be able to program in at least one of these languages. Alternately, you will be responsible for finding and learning a toolkit that requires programming in a language you are comfortable with,
1. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability. ### Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.

11-785 Introduction to Deep Learning

Deep Learning

Probability

Structure Learning

Policy Search

Beliefs

Multiagent Reasoning

Inference

Exact Solutions

Actor Critic

Offline Belief State Planning

Parameter Learning

Online Planning

Model-Based and Model-Free Reinforcement Learning

Controller Abstractions

Simple Decisions

Policy Gradient Estimation and Optimization

Exact Belief State Planning

Policy Validation, Implications, and Summary

Bayesian Networks Representation and Inference

Approximate Value Functions

Exploration / Exploitation

Online Belief State Planning

AA 228 / CS 238 Decision Making under Uncertainty Stanford University Winter 2023 The course introduces decision making under uncertainty from a computational perspective, covering dynamic programming, reinforcement learning, and more. Prerequisites include basic probability and fluency in a high-level programming language. This course introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration. - You will gain a broad fundamental understanding of the mathematical models and solution methods for decision making (exercises, three quizzes).
- You will be able to implement and extend key algorithms for learning and decision making (two programming projects).
- You will be able to identify an application of the theory in this course and formulate it mathematically (proposal).
- You will gain a deep understanding of an area of particular interest and apply it to a problem (final project).
- You will be able to critique approaches to solving decision problems (peer review). basic probability and fluency in a high-level programming language. This year we will be using the textbook titled *Algorithms for Decision Making*, MIT Press, 2022. Printed copies are available for purchase or you may download it for free as a PDF. Details are [here](https://algorithmsbook.com/decisionmaking).

The textbook serves as the official lecture notes.

AA 228 / CS 238 Decision Making under Uncertainty

Artificial Intelligence

Representations

2 courses cover this concept

11-785 Introduction to Deep Learning

AA 228 / CS 238 Decision Making under Uncertainty