Introduction

What's ML? | What’s Deep Learning? | About This Courses | FAQ

Slides Notation

Scientific Python 101 (No Assignment)

This lab guides you through the setup of scientific Python environment and provides useful references for self-reading.

Notebook

Linear Algebra

Span & Linear Dependence | Norms | Eigendecomposition | Singular Value Decomposition | Traces & Determinant

Video Slides

Data Exploration & PCA (Bonus)

This lab guides you through the process of Exploratory Data Analysis (EDA) and discuss how you can leverage the Principle Component Analysis (PCA) to visualize and understand high-dimensional data.

Notebook

Probability & Information Theory

Random Variables & Probability Distributions | Multivariate & Derived Random Variables | Bayes’ Rule & Statistics | Principal Components Analysis | Technical Details of Random Variables | Common Probability Distributions | Common Parametrizing Functions | Information Theory | Decision Trees & Random Forest

Video Slides

Decision Trees & Random Forest (Bonus)

In this lab, we will apply the Decision Tree and Random Forest algorithms to the classification and dimension reduction problems using the Wine dataset.

Notebook

Numerical Optimization

Numerical Computation | Optimization Problems | Gradient Descent | Newton's Method | Stochastic Gradient Descent | Perceptron | Adaline | Constrained Optimization | Linear & Polynomial Regression | Generalizability & Regularization | Duality

Video Slides

Perceptron & Adaline (Bonus)

In this lab, we will guide you through the implementation of Perceptron and Adaline, two of the first machine learning algorithms for the classification problem. We will also discuss how to train these models using the optimization techniques.

Notebook

Regression (Bonus)

This lab guides you through the linear and polynomial regression using the Housing dataset. We will also extend the Decision Tree and Random Forest classifiers to solve the regression problem.

Notebook

Learning Theory & Regularization

Learning Theory | Point Estimation | Bias & Variance | Consistency | Decomposing Generalization Error | Weight Decay | Validation

Slides

Regularization

In this lab, we will guide you through some common regularization techniques such as weight decay, sparse weight, and validation.

Notebook Slides

Probabilistic Models

Probabilistic Models | Maximum Likelihood Estimation | Linear Regression | Logistic Regression | Maximum A Posteriori Estimation | Bayesian Estimation and Inference | Gaussian Process

Slides

Logistic Regression & Metrics

In this lab, we will guide you through the practice of Logistic Regression. We will also introduce some common evaluation metrics other than the "accuracy" that we have been used so far.

Notebook Slides

Neural Networks from Scratch (No Assignment)

In this tutorial, you will learn the fundamentals of how you can build neural networks without the help of the deep learning frameworks, and instead by using NumPy.

Notebook

TensorFlow 101 (No Assignment)

We are going to use TensorFlow as our framework in the following lectures. In this lab, you will learn how to install TensorFlow and get a better understanding by implementing a classical deep learning algorithm.

Notebook Slides

Non-Parametric Methods & SVMs (Suggested Reading)

KNNs | Parzen Windows | Local Models | Support Vector Classification (SVC) | Slacks | Nonlinear SVC | Dual Problem | Kernel Trick

Slides

SVMs & Scikit-Learn Pipelines (Bonus)

In this lab, we will classify nonlinearly separable data using the KNN and SVM classifiers. We will show how to pack multiple data preprocessing steps into a single Pipeline in Scikit-learn to simplify the training workflow.

Notebook

Cross Validation & Ensembling (Suggested Reading)

Cross Validation | How Many Folds? | Voting | Bagging | Boosting | Why AdaBoost Works?

Slides

CV & Ensembling (Bonus)

In this lab, we will guide you through the cross validation technique for hyperparameter selection. We will also practice and compare some ensemble learning techniques.

Notebook

Neural Networks: Design

NN Basics | Learning the XOR | Back Propagation | Cost Function & Output Neurons | Hidden Neurons | Architecture Design & Tuning

Slides

Neural Networks: Optimization & Regularization

Momentum & Nesterov Momentum | AdaGrad & RMSProp | Batch Normalization | Continuation Methods & Curriculum Learning | NTK-based Initialization | Cyclic Learning Rates | Weight Decay | Data Augmentation | Dropout | Manifold Regularization | Domain-Specific Model Design

Slides

Word2Vec

In this lab, we will introduce a neural network, called the word2vec, that embeds words into a dense vector space where semantically similar words are mapped to nearby points.

Notebook Slides

Convolutional Neural Networks

Convolution Layers | Pooling Layers | Variants & Case Studies | Visualizing Activations | Visualizing Filters/Kernels | Visualizing Gradients | Dreaming and Style Transfer | Segmentation and Localization | Object Detection | More Applications

Slides

Convolutional Neural Networks & Data Pipelines

In this lab, we will introduce two datasets, MNIST and CIFAR-10, then we will talk about how to implement CNN models for these two datasets using tensorflow. Then offer a guide to illustrate typical input pipeline of TensorFlow 2.0.

Notebook Slides

Visualization, Style Transfer & Save and Load Models

This lab guides how to load and use a pretrained VGG19 model and how to visualize what the CNN networks have learned in selected layers. This also introduces an interesting technique called "Style Transfer" and displays galleries of its creative outputs. Last but not least, we will also demonstrate how to save and load model during training and explain the TensorFlow family briefly.

Notebook Slides

Recurrent Neural Networks & Transformers

Vanilla RNNs | Design Alternatives | Backprop through Time (BPTT) | Optimization Techniques | Optimization-Friendly Models & LSTM | Parallelism & Teacher Forcing | Attention | Explicit Memory | Adaptive Computation Time (ACT) | Visualization | Memory Networks | Google Neural Machine Translation | Transformers | Subword Tokenization |

Slides

Seq2Seq Learning for Machine Translation

This lab guides how to use recurrent neural networks to model continuous sequence like nature language, and use it on not only article comprehension but also word generation.

Notebook

Image Caption

In this lab, we introduce how to design a model that can be given an image, and then generates suitable caption which can describe the image. To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption.

Notebook Slides

Unsupervised Learning & Generative AI

Text Models & Image Models | Clustering | Factorization | Dimesion Reduction | ChatGPT | Autoencoders & Manifold Learning | Variational Autoencoders (VAE) | Flow-based Models | Diffusion Models | Generative Adversarial Networks (GANs)

Slides