Calculus and Optimization for Machine Learning
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Approx. 32 hours to complete
Course Summary
This course teaches the essential concepts of calculus and optimization needed for machine learning. It covers topics like differentiation, optimization, and gradient descent, and how they can be applied in machine learning algorithms.Key Learning Points
- Understand the fundamental concepts of calculus and optimization
- Learn how to apply calculus and optimization in machine learning algorithms
- Gain hands-on experience with programming exercises
Job Positions & Salaries of people who have taken this course might have
- Machine Learning Engineer
- USA: $120,000
- India: ₹1,200,000
- Spain: €40,000
- Data Scientist
- USA: $110,000
- India: ₹1,000,000
- Spain: €35,000
- AI Researcher
- USA: $150,000
- India: ₹1,500,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand the fundamental concepts of calculus and optimization
- Apply calculus and optimization techniques in machine learning algorithms
- Implement programming exercises to gain hands-on experience
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of linear algebra and probability theory
- Experience with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Programming exercises
- Video lectures
- Quizzes
Similar Courses
- Applied Data Science with Python
- Machine Learning
- Neural Networks and Deep Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses.
Outline
- Introduction: Numerical Sets, Functions, Limits
- About the University
- Welcome to the Course!
- Introduction to the Week
- Numerical Sets
- Mappings and Quantifiers
- Functions : Definitions
- Functions: Arithmetic and Composition
- Graph Transformations
- Limit of Sequences
- Limit of Sequences: Examples
- Limit of Sequences: Arithmetic Rules
- Limits of Sequences: Definition of e
- The Indeterminate Forms
- Comparison between Polynomial, Exponential and Logarithmic Functions
- About University
- Rules on the academic integrity in the course
- Real numbers
- Newton's Binomial Theorem
- The Definition of e
- Practice Quiz #1
- Practice Quiz #2
- First Week Final Test
- Limits and Multivariate Functions
- Introduction to the Week
- From Sequences to Functions
- Function’s Limit
- Function’s Limit: Examples
- Function’s Limit: Arithmetic
- Function’s Limit: Indeterminate Forms and Boundary Rule
- Two Important Limits
- Asymptotic Notations
- Big-O
- Little-o
- Multivariate Functions: Definitions
- Multivariate Functions: Limits
- Multivariate limits: Examples
- Practice Quiz #1
- Practice Quiz #2
- Second Week Final Test
- Derivatives and Linear Approximations: Singlevariate Functions
- Introduction to the Week
- Derivative: Definition
- Differentiability
- Derivatives: Examples
- Arithmetic of Derivatives
- Derivatives: Chain Rule
- Tangent Line: Equation
- Linear Approximations
- Mean Value Theorem
- Second Derivatives
- Convexity
- Extrema and First Derivative
- Extrema and Second Derivative
- Derivatives: Logarithmic Rule
- Derivatives: Inverse Functions
- Extrema: Clearing the Air
- Practice Quiz #1
- Practice Quiz #2
- Practice Quiz #3
- Third Week Final Test
- Derivatives and Linear Approximations: Multivariate Functions
- Introduction to the Week
- Tangent Plane: Motivation
- Partial Derivatives: Definition
- Partial Derivatives: Examples
- Tangent Plane: Definition
- Differentiability of Multivariate Function
- Differentiability of Multivariate Function: Example
- Differentiability: Sufficient Condition
- Basic Geometry and Gradient
- Chain Rule: One Independent Variable
- Second Partial Derivatives
- Differentials of Multivariate Functions
- Convexity
- Second Partial Derivatives: Convexity
- Convexity: clearing the air
- Practice Quiz #1
- Practice Quiz #2
- Forth Week Final Test
- Integrals: Anti-derivative, Area under Curve
- Introduction to the Week
- Antiderivative: Definition
- Antiderivative: Table of Integrals
- Change of Variable and Integration by Parts
- Antiderivative: Examples
- Integrability in Elementary Functions
- Definite Integral
- Definite Integral: Existence
- Definite Integral: Sufficient Conditions
- Fundamental Theorem of Calculus
- Improper Integrals
- Improper Integrals: Examples and Comparison Rule
- Numerical Methods of Integration
- Practice Quiz #1
- Practice Quiz #2
- Fifth Week Final Test
- Optimization: Directional derivative, Extrema and Gradient Descent
- Introduction to the Week
- Directional Derivative: Definition
- Directional Derivative: Calculation
- Direction of Maximal Growth
- Multivariate Extrema
- Gradient Descent
- Introduction to the Final Project
- Models and Parameters: Clearing the Air
- Practice Quiz #1
- Practice Quiz #2
Summary of User Reviews
Discover the secrets of calculus and optimization for machine learning in this highly rated course. Users have praised the course for its comprehensive coverage of the subject matter and hands-on approach.Key Aspect Users Liked About This Course
The hands-on approach to learning calculus and optimization for machine learning is a key aspect that many users have praised.Pros from User Reviews
- Comprehensive coverage of calculus and optimization for machine learning
- Hands-on approach to learning
- Highly knowledgeable and engaging instructors
- Great resources and support from the Coursera community
- Opportunities to apply the concepts learned to real-world machine learning problems
Cons from User Reviews
- Some users found the course content to be too difficult for beginners
- The course requires a significant time commitment
- Limited interaction with instructors and peers
- The course may be too theoretical for some users
- The course could benefit from more practical examples and exercises
Anton Savostianov
4 Raiting
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