Week Lecture Topic
PART I: Introduction

1

8/29

 Course Introduction

  • The instructor, the outline, classroom conduct, academic integrity, attendance, and grading policy.

An Introduction to AI and Its History

  • Different Views  and Approaches to AI
  • AI vs ML vs DL
  • AI in Industry
  • AI in Enterprise

2

9/5**

 **No class. Classes Following Monday Schedule
PART II: Machine Learning

3

9/12

 Introduction to Supervised Learning

  • K-Nearest Neighbor (KNN)
  • Model Generation
  • Train and Test Splitting

4

9/19

 EXAM I (PART I and Supervised Learning)

Linear Regression

  • Linear Regression with one variable
  • Model Representation
  • Cost Function
  • Parameter Learning (Gradient Descent)
  • Multivariate Linear Regression
  • Gradient Descent for Multiple Features Normal Equation
  • Polynomial Regression
5

9/26

 Logistic Regression

  • Classification
  • Hypothesis Representation
  • The Problem of Overfitting
  • Decision Boundary
  • Cost Function
  • Advanced Optimization
  • Multiclass Classification

6

10/3

 EXAM  II (PART II Linear and Logistic Regression)

Naïve Bayes

  • Basics probability theory
  • The Naïve Bayes classifier

7

10/10

 Support Vector Machines

  • Optimization Objective
  • Large Margin Intuition Kernels
8

10/17

 EXAM III (PART II Naïve Bayes and SVM)

Introduction to Unsupervised Learning

  • Clustering with K-Means
  • Hierarchical Agglomerative Clustering
Part III: Deep Learning

9

10/24

 Introduction to Deep Learning:

  • Neural Networks
  • Convolutional Neural
  • Transfer Learning

10

10/31

 EXAM IV (PART II Unsupervised Learning and Part III Intro Deep Learning)

Neural Networks: Representation

  • Non-linear Hypotheses
  • Neurons and the Brain
  • Model Representation
  • Example and Intuition
  • Multiclass Classification

11

11/7

 Neural Networks: Learning

  • Learning Cost Function
  • Backpropagation Algorithm
  • Backpropagation Intuition

12

11/14

 Deep Reinforcement Learning

  • Q-Learning
  • Deep Reinforcement Learning
  • Policies and Learning Algorithms
PART IV: Final project (Autonomous Self Driving Car)

13

11/21

 EXAM V (PART III Deep Learning and Deep Reinforcement Learning)

Project hours (Assembling the Car)
14 11/28** **No class. Happy Thanks Given!!

15

12/5

 Project hours (Training)

16

12/12

 Project hours (Adjustments)

17

12/19

 Final project presentation and Autonomous Vehicles Race (City Tech AV Cup)