Perceptron

Informed Search

Probability

Decision Networks

Search with Other Agents

Hidden Markov Model (HMM)

Optimization

Bayes' Nets: Independence

Constraint Satisfaction Problem (CSP)

Value of Information (VPI)

Logistic Regression

Bayes' Nets: Representation

Markov Decision Process (MDP)

Particle Filtering

Neural network

Bayes' Nets: Inference

Uninformed Search

Reinforcement learning (RL)

Naive Bayes

Bayes' Nets: Sampling

CS 188 Introduction to Artificial Intelligence UC Berkeley Fall 2022 UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings. This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.

By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

See the syllabus for slides, deadlines, and the lecture schedule. Readings refer to [fourth edition of AIMA](http://aima.cs.berkeley.edu/) unless otherwise specified.  - **CS 61A or 61B**: Prior computer programming experience is expected (see below)
- **CS 70 or Math 55**: Familiarity with basic concepts of propositional logic and probability are expected (see below)

*CS61A AND CS61B AND CS70 is the recommended background.*

The required math background in the second half of the course will be significantly greater than the first half. The self-diagnostic assignment Homework 0 will help check your preparation.

Course programming assignments will be in Python. We do not assume that students have previous experience with the language, but we do expect you to learn the basics very rapidly. Project 0 is designed to teach you the basics of Python and how the CS 188 submission autograder works. Project 1 is a good representation of the programming level that will be required for subsequent projects in this class. 

CS 188 Introduction to Artificial Intelligence

Artificial Intelligence

Decision Tree Learning

Margins

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

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)

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

Maximum A Posteriori (MAP)

Kernel (operating system)

Geometric Margins

Sample Complexity

Structural Risk Minimization

Maximizing Data Likelihood

Unsupervised learning

Dimensionality Reduction

Sampling Bias

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

Supervised learning

Classification

Training/Validation/Testing

MNIST

Loss function

Automatic differentiation

Matrix representation of Neural Networks

Graphics Processor (GPU)

TensorFlow

Multilayer perceptron

Activation Functions

Regularization (mathematics)

Language Models

Word Embeddings

Feedforward neural network

Recurrent neural network (RNN)

Long Short-Term Memory (LSTM)

Gated recurrent unit (GRU)

Machine Translation

Attention (machine learning)

Transformer (machine learning model)

Scaling Deep Learning Systems

Interpretation of Neural Networks

Autoencoders

Variational autoencoder (VAE)

Generative adversarial network (GAN)

Deepfake

Deep Q-learning

Policy Gradient Methods

Actor-Critic Methods

Graph neural network (GNN)

CSCI 1470/2470 Deep Learning Brown University Spring 2022 Brown University's Deep Learning course acquaints students with the transformative capabilities of deep neural networks in computer vision, NLP, and reinforcement learning. Using the TensorFlow framework, topics like CNNs, RNNs, deepfakes, and reinforcement learning are addressed, with an emphasis on ethical applications and potential societal impacts. Over the past few years, Deep Learning has become a popular area, with deep neural network methods obtaining state-of-the-art results on applications in computer vision (Self-Driving Cars), natural language processing (Google Translate), and reinforcement learning (AlphaGo). These technologies are having transformative effects on our society, including some undesirable ones (e.g. deep fakes).

This course is there to give students a practical understanding of how Deep Learning works, how to implement neural networks, and how to apply them ethically. We introduce students to the core concepts of deep neural networks and survey the techniques used to model complex processes within the contexts of computer vision and natural language processing.

Throughout the course, we emphasize and require students to think critically about potential ethical pitfalls that can result from mis-application of these powerful models. The course is taught using the Tensorflow deep learning framework.
 By the end of this course, you will be able to:

- Learn about the fundamental algorithms that underly all modern deep learning models.
- Implement different types of deep learning models in Tensorflow.
- Think critically about using a deep learning model for a task and its potential societal impact.
- Collaborate with classmates on a team project to apply deep learning models to task of your
choice.
- Communicate your findings (both positive and negative results are encouraged) through pre-
sentations. - A basic programming course: (CSCI 0150, 0170 or 0190)
- A linear algebra course: (CSCI 0530, MATH 0520 or 0540)
- A stats / probability course: (CSCI 0220, 1450, 0450, MATH 1610, APMA 1650 or 1655)

Exceptions may be possible for those missing one of these prerequisites if (a) the student has taken
another course which covers similar material, or if (b) the student will be concurrently taking the
prerequisite. If either of these situations applies to you, use the “Request Override” feature in
Courses@Brown to request an override code (and explain why you believe your situation merits
one). ### Textbook

None required. Students are encouraged to refer to the following textbook, which is available online:

- [Deep Learning](https://www.deeplearningbook.org/), by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

CSCI 1470/2470 Deep Learning

Deep Learning

Perceptron

3 courses cover this concept

CS 188 Introduction to Artificial Intelligence

10-401 Introduction to Machine Learning

CSCI 1470/2470 Deep Learning