ที่

ชื่อวิชา/เนื้อหา

ระยะเวลา

รูปแบบ

1

Module 1

AI Engineering Basics

1.1. Python Programming for AI (6h)

1.1.1. Basic Python Programming

1.1.2. Data Processing Libraries

1.1.3. Data Visualization Libraries

1.1.4. Python Libraries for Machine Learning

1.2. Data Processing Fundamentals for AI (6h)

1.2.1. Exploring Data

1.2.2. Data Filtering & Transformation

1.2.3. Feature Extraction

1.2.4. Dimensionality Reduction

1.3. Machine Learning Fundamentals (6h)

1.3.1. Decision Tree Learning

1.3.2. Bayesian Learning

1.3.3. Nearest-neighbor Algorithms

1.3.4. Artificial Neural Networks

1.4. Statistical Pattern Recognition (6h)

1.4.1. K-Nearest Neighbors

1.4.2. Radial Basis Functions & Support Vector Machines

1.4.3. Cluster Analysis

1.4.4. Regression Analysis

4 สัปดาห์

บรรยาย

2

Module 2

AI Engineering Professionals

2.1. Overview of Deep Learning (6h)

2.1.1. Understanding Deep Learning Architectures

2.1.2. Overview of TensorFlow & Keras

2.1.3. Using Pre-trained TensorFlow Models

2.1.4. Training Deep Learning Models Using TensorFlow & Keras

2.2. End-to-end AI Platform Implementation (6h)

2.2.1. Data Ingestion & Cloud Data Processing

2.2.2. Using AI Cloud Engines

2.2.3. Model Training, Testing & Validation

2.2.4. Integrating AI with Frontend Applications

2.3. Deep Learning & Computer Vision (6h)

2.3.1. Collecting & Processing Images/Videos

2.3.2. CNN Architectures for Object Detection & Classification

2.3.3. Model Training, Testing & Validation

2.3.4. Building a Simple Image Classification Applications

2.4. Deep Learning & Text Processing (6h)

2.4.1. Collecting & Processing Text Data

2.4.2. RNN Architectures for Text Classification & Understanding

2.4.3. Model Training, Testing & Validation

2.4.4. Building a Simple Text Classification Applications

4 สัปดาห์

บรรยาย

3

Module 3

Project-based Internship with Companies

ผู้เรียนได้ลงมือปฏิบัติจริงและประสบการณ์ทำงานจริงจากการฝึกงานในบริษัททางด้านระบบปัญญาประดิษฐ์  (AI : Artificial Intelligence)

48 ชั่วโมง (8 สัปดาห์)

ปฎิบัติ