CS565600 Deep Learning

Fundamentals of machine learning, deep learning, and AI.

Description

This class introduces the concepts and practices of deep learning. The course consists of three parts. In the first part, we give a quick introduction to classical machine learning and review some key concepts required to understand deep learning. In the second part, we discuss how deep learning differs from classical machine learning and explain why it is effective in dealing with complex problems such as image and natural language processing. Various CNN and RNN models will be covered. In the third part, we introduce deep reinforcement learning and its applications.

This course also gives coding labs. We will use Python 3 as the main programming language throughout the course. Some popular machine learning libraries such as Scikit-learn and Tensorflow 2.0 will be used and explained in details.

Syllabus

Time & Location

  • Tue. 3:30-5:20pm at Delta 107
  • Thu. 3:30-4:20pm at Delta 107
  • Office hour: Thu. 4:20pm-5:30pm at Delta 724

Grading Policy

  • Quiz: 20%
  • Lab Assignments: 40%
  • Competition: 40%
  • Bonus: 5%

Prerequisites

This course is intended for senior undergraduate and junior graduate students who have a proper understanding of

  • Python Programming Language
  • Calculus
  • Linear Algebra
  • Probability Theory
Although it would be helpful, knowledge about classical machine learning is NOT required.

IMPORTANT

Students will group (2~4 people a group). This class requires each group of students to prepare a GPU card to perform the necessary computing. You can follow this link to decide which GPU card to go for. NO GPU CARD PROVIDED IN THE CLASS.

Announcement

  • 2020/10/21 - Lab 08: notebook updated

  • 2020/10/21 - Lecture 11: slides and video released

  • 2020/10/21 - Lab 10: notebook released

  • 2021/10/14 - Lecture 10: slides and video released

  • 2020/10/14 - Contest 01: slides and notebook released

  • 2020/10/14 - Lab 08: notebook released

  • 2020/10/14 - Lab 07: notebook released

  • 2020/10/14 - Lab 06: slides and notebook released

  • 2020/10/12 - Lecture 06 ~ 08: slides and video released

  • 2020/10/07 - Lab5: notebooks released

  • 2020/10/04 - Lecture 05: slides and video released

  • 2021/09/30 - Lecture 01 ~ 04 : slides and video released

  • 2021/09/14 - There will be NO class on 9/13~9/21. Instead, video for Lec.2~4 is prepared on our website. A exam which based on Lec.2~4 will be held in class on 9/23, please prepare by yourself.

  • 2021/09/09 - Lecture 01 ~ 04 : slides and video released

Curriculum

If you have any feedback, feel free to contact: shwu [AT] cs.nthu.edu.tw

Lecture 01

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

Lecture 02

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

Lecture 03

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

Lecture 04

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

Lecture 05

Learning Theory & Regularization

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

Video Slides

Regularization

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

Notebook Slides

Lecture 06

Probabilistic Models

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

Video 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

Lecture 07

Non-Parametric Methods & SVMs (Suggested Reading)

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

Video 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

Lecture 08

Cross Validation & Ensembling (Suggested Reading)

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

Video 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

Competition 01

Predicting News Popularity

In this competition, you are provided with a supervised dataset consisting of the raw content and binary popularity of news articles. What you need to do is to learn a function that is able to predict the popularity of an unseen news article.

Notebook Slides Kaggle

Lecture 10

Neural Networks: Design

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

Video 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

Lecture 11

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

Video Slides

Resources

Following provides links to some useful online resources. If this course starts your ML journey, don't stop here. Enroll yourself in advanced courses (shown below) to learn more.

Other Course Materials

For more course materials (such as assignments, score sheets, etc.) and online forum please refer to the eeclass system.

eeclass System

Reference Books

  • Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016, ISBN: 0387848576

  • Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, Springer, 2009, ISBN: 0387848576

  • Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006, ISBN: 0387310738

  • Sebastian Raschka, Python Machine Learning, Packt Publishing, 2015, ISBN: 1783555130

Online Courses