Section 1: Introduction

• Lecture 1: Introduction

• Lecture 2: Course Structure

• Lecture 3: Common AI Applications in Engineering Companies

• Lecture 4: Course Requirements

• Lecture 5: Installing Anaconda

Section 2: Optimization - Fundamentals

• Section 2 Files

• Lecture 6: General Optimization Techniques

• Lecture 7: Greedy Randomized Adaptive Search Procedure (GRASP)

• Lecture 8: Imports, Data input

• Lecture 9: Cost, Seed Functions

• Lecture 10: Ranking Function

• Lecture 11: Local Search

• Lecture 12: PartA: Restricted Candidate List (RCL)

• Lecture 13: PartB: Restricted Candidate List (RCL)

• Lecture 14: Main Iteration

Section 3: Optimization - Application

• Section-3 Files

• Lecture 15: Job Shop Problem

• Lecture 16: Integer Linear Programming

• Lecture 17: set needed data

• Lecture 18: set variables

• Lecture 19: set constraints

• Lecture 20: set objective

• Lecture 21: solve results

Section 4: Structured Data - Fundamentals

• Section-4 Files

• Lecture 22: Supervised and Unsupervised Machine Learning

• Lecture 23: K-means Clustering

• Lecture 24: import libraries

• Lecture 25: Data Preprocessing

• Lecture 26: Calculate Distance

• Lecture 27: Centroid Initialization

• Lecture 28: Main Loop

• Lecture 29: Results Assessment

Section 5: Structured Data - Application

• Section-5 Files

• Lecture 30: Download the Data

• Lecture 31: Understand the General Data

• Lecture 32: Data Exploration

• Lecture 33: Data Arrangement

• Lecture 34: Data Preparation

• Lecture 35: KNN (K-Nearest Neighbors)

• Lecture 36: Support Vector Machine (SVM)

• Lecture 37 : Random Forest

Section 6: Reinforcement Learning - Fundamentals

• Section-6 Files

• Lecture 38: Reinforcement Learning Fundamentals

• Lecture 39 : Environment

• Lecture 40: Settings

• Lecture 41: Main Loop

Section 7: Reinforcement Learning - Application

• Section-7 Files

• Lecture 42: Coding RL Deep Q Learning

• Lecture 43: Coding RL using OpenAI-Baselines

Section 8: Computer Vision - Fundamentals

• section8.zip

• Lecture 44:Deep Learning

• Lecture 45:Convolutional Neural Network (CNN)

• Lecture 46: Data Preprocessing

• Lecture 47: Build/Training the model

• Lecture 48: Results

Section 9: Computer Vision - Application

• section_9.zip

• Lecture 49: Data Preprocessing_Part1

• Lecture 50: Data Preprocessing_Part2

• Lecture 51: Training

• Lecture 52: Results