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 of 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 the image and natural language processing. Various CNN and RNN models will be covered. In the third part, we introduce the 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 will be used and explained in detials.

Syllabus (Tentative)

Instructor

Teaching Assistants

Time & Location

  • Tue. 3:30-5:20pm at Delta 107
  • Thu. 3:30-4:20pm at Delta 107
  • Office hour: Wed. 3-5pm at Delta 723/724

Grading Policy

  • Competitions (x5): 60%
  • Lab Quiz: 40%

Prerequisites

This course is intended for senior undergraduate and junior graduate students who understand

  • Computer Programming (Python),
  • Calculus,
  • Linear Algebra, and
  • Probability.
Although not required, background knowledge about scientific computing and classic machine learning will be helpful.

Announcement

  • 2018/10/18 - Lecture 11: slides and videos announced

  • 2018/10/18 - Lecture 10: notebook announced

  • 2018/10/16 - Contest 01: competition announced

  • 2018/10/12 - Lecture 10: slides and videos announced

  • 2018/10/12 - Lecture 09: slides and videos announced

Curriculum

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

Lecture 01

Introduction

What's ML? | How DL Differs from Classic ML? | About This Courses | FAQ

Slides Notation

Scientific Python 101

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

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 | Information Theory | Decision Trees & Random Forest

Video Slides

Decision Trees & Random Forest

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 | Unconstrained Optimization | Stochastic Gradient Descent | Perceptron | Adaline | Constrained Optimization | Linear & Polynomial Regression | Duality

Video Slides

Perceptron & Adaline

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

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

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

Lecture 06

Probabilistic Models

Maximum Likelihood Estimation | Maximum A Posteriori Estimation | Bayesian Estimation

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

Lecture 07

Non-Parametric Methods & SVMs

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

Video Slides

SVMs & Scikit-Learn Pipelines

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

CV | How Many Folds? | Voting | Bagging | Boosting | Why AdaBoost Works?

Video Slides

CV & Ensembling

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 Appropriate Response

In this competition, you have to select the most appropriate response from 6 candidates based on previous chat message. Your goal is to learn a function that is able to predict the best response.

Notebook

Lecture 09

Large-Scale Machine Learning

When ML Meets Big Data... | Representation Learning | Curse of Dimensionality | Trade-Offs in Large-Scale Learning | SGD-Based Optimization

Video Slides

Lecture 10

Neural Networks: Design

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

Video Slides

TensorFlow101 & Word2Vec

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

Notebook

Lecture 11

Neural Networks: Optimization & Regularization

Momentum & Nesterov Momentum | AdaGrad & RMSProp | Batch Normalization | Continuation Methods & Curriculum Learning | 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 iLMS system.

iLMS 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