Overfitting

Supervised learning

Regularization (mathematics)

Unsupervised learning

Latent Factor Models

Policy Iteration

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)

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

Connectionist Machines

McCullough and Pitt model

Hebb’s learning rule

Rosenblatt’s perceptron

Universal Approximator

Perceptron learning rule

Empirical risk minimization

Optimization

Gradient descent

Back-propagation

Momentum

Nestorov

Convergence

Learning Rates

Optimization Algorithms

RMSProp

Stochastic gradient descent (SGD)

Acceleration

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)

Representations

Autoencoders

Hopfield Networks

Boltzmann Machines

Normalizing Flows

Variational autoencoder (VAE)

Generative adversarial network (GAN)

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

Decision Tree Learning

Margins

Naive Bayes

Maximizing Conditional Likelihood

Kernelizing Algorithms

Primal and Dual Forms

Finite Hypothesis Classes

k-fold cross-validation

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

Conditional Independence

Kernelizing Perceptron

Kernelizing SVM

VC Dimension Based Bounds

Backpropagation

Boosting

Interactive Learning

Transductive SVM

Bayes' theorem

Perceptron

Maximum A Posteriori (MAP)

Logistic Regression

Kernel (operating system)

Geometric Margins

Sample Complexity

Structural Risk Minimization

Maximizing Data Likelihood

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

Classification

Training/Validation/Testing

MNIST

Loss function

Automatic differentiation

Matrix representation of Neural Networks

Graphics Processor (GPU)

TensorFlow

Multilayer perceptron

Activation Functions

Language Models

Word Embeddings

Feedforward neural network

Gated recurrent unit (GRU)

Machine Translation

Attention (machine learning)

Transformer (machine learning model)

Scaling Deep Learning Systems

Interpretation of Neural Networks

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

Overfitting

4 courses cover this concept

COS 324: Introduction to Machine Learning

11-785 Introduction to Deep Learning

10-401 Introduction to Machine Learning

CSCI 1470/2470 Deep Learning