NUS Curriculum

Introduction to Data Analytics
  • What is Data Analytics?
  • Types of Data Analytics
  • Data in Data Analytics
  • Decision Models
  • Data Mining Process
  • Overview of Predictive Analytics
Exploratory Data Analysis
  • Data Visualisation
  • Data Querying
  • Statistical Methods for Summarizing Data
  • Exploring Data Using Pivot Tables
Descriptive Statistical Measures
  • What is Descriptive Analytics?
  • Populations and Samples
  • Measures of Location
  • Measures of Dispersion
  • Measures of Shape
  • Measures of Association
Introduction to Python Data Science Libraries
  • Numpy
  • Scipy
  • Matplotlib
  • Sci-kit Learn
Introduction to Regression Analysis
  • Simple Linear Regression
  • Multi Linear Regression
  • Stepwise Regression
  • Coding Scheme for Categorical Variables
  • Problems with Linear Regression
Introduction to Classification
  • Decision Trees
  • Bayesian Classifier
  • Logistic Regression
  • Multinomial Logistic Regression
  • Support Vector Machine
  • Separating Hyperplane
  • Maximal Margin Classifier
  • Support Vector Classifier
Introduction to Clustering
  • Affinity Measures and Partition Methods
  • K-means
  • K-medoids
  • Hierarchical Methods
Introduction to Association

•  Structure and Representation of Association Rules
•  Strong Association Rules and the Concept of Frequent Itemsets
• Apriori Algorithm
• FP Growth
• Time Series Analysis

Introduction to Text Mining

• Text Mining Terminologies
• Text Mining Concepts
• Text Mining Process
• Creating the Corpus
• Creating the Term-Document Matrix
• Extracting the Knowledge
• Knowledge Extraction Methods for Text Mining
• Classification
• Clustering
• Association

 

Overview of ANN

• Break-through Applications with ANN
• Why ANN?
• Problems of Logistic Regression
• Back-propagation
• Gradient Descent Algorithm (GD)

 

Difficulties of training ANN

• Poor Gradient
• Overfitting and Underfitting

 

Advanced GD algorithm

• Stochastic GD (SGD)
• Mini-batch SGD
• Momentum SGD
• RMSprop and Adam

Other Training Techniques of ANN

• Random Initialization
• ReLU
• Dropout
• Data Augmentation

 

HPE Curriculum

Pre-trained Models
  • • Image classification
  • • Object detection
  • • Word embedding
Optimization and Tuning
  • • Learning rate
  • • Momentum
  • • Optimization algorithm
  • • Parameter initialization strategy
  • • Data normalization
  • • Batch normalization
  • • Hyperparameter tuning strategy
  • • Hardware acceleration
  • • HPE Deep Learning Cookbook

 

Deep Learning Project Management
  • • Deep learning project management
  • • Data acquisition
  • • Data preprocessing
  • • Data labelling
  • • Baselining
  • • Data augmentation
  • • Transfer learning
  • • Performance measurement
  • • Ensemble method

 

Applications of AI in
  • • Fintech
  • • Social Media
  • • Security
Capstone Project
  • • Apply deep learning to a real-life use case




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