lanceshih / syllabus

1. Python and Programming

• Python Tutorial

• Python Cookbook

  • Chapter 1
  • Chapter 2
  • Chapter 5: 1, 2, 5
  • Chapter 6: 1, 2
  • Chapter 7: 1, 2, 4, 6, 11

• Python OOP

2. Data Structure and Algorithms

• Linked List
• Sorting
  • QuickSort
• Heap
  • HeapSort
• Tree
  • Tree examples: HTML, JSON
  • Tree Traversal
  • BST
  • k-NN Search with KD Treesklearn
• Graph
  • Graph Traversal
  • Social Networks
  • PageRank
  • k-NN Search with Graph
  • Search in AI, II.3, II.5, II.6
• Hash Table
  • Inverted Index
  • TF.IDF
  • k-NN Search with LSH
• Dynamic Programming
  • Hidden Markov Model
  • Viterbi Algorithm

3. Systems and Architectures

• Memory Hierarchy and Cache
  • BLAS
  • BLAS and Cache
• File System
• Networks
  • Basic Networks, MetaSploit Chapter 3
  • NetCat
  • HTTP

4. Maths

4.1 Linear Algebra

• Numpy Quickstart
• Numpy Basics
• SciPy Linear Algebra
• Linear Programming

4.2 Statistics

• Basic Statistics, 1-3 (materials before Chapter 1)

5. Machine Learning

5.1 Introduction

• Background, PRML Chapter 1
• Sklearn Quick Start

5.2 Data Loading

• Data Loading 5.1-5.3, 5.4.1, 5.5

5.3 Regression

• Linear regression,
• Linear regression, GDTR Chapter 3.1
• sklearn regression 1.1.1-1.1.3, 1.1.11, 1.1.12,

5.4 Classification

• GDTR Chapter 4.1-4.3
• SVM
• Nearest Neighbors
• Decision Trees

5.5 Evaluation

• Cross-validation
• GDTR Chapter 5

5.6 Clustering

• Clustering
• Gaussian Mixture

5.7 Dimension Reduction

• Dimension Reduction
• PCA

5A. Machine Learning Syllabus

6. Computer Vision

OpenCV

• Core Operations
• Image Processing

7. Deep Learning

• Neural Networks, PRML 5.1-5.3

8. Database and Big Data

SQL

• MySQL Tutorial

Spark

• Spark
• Spark SQL

9. Web Programming

• HTML
• Flask
• Django
• JavaScript

Other Resources

Online Master of Science in Computer Science （OMSCS）

Current Courses

Specializations