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Dhawal Shah, 27 Apr 2018, Quartz, Here are 300 free Ivy League university course you can take online right now, here. Math and Progamming listings are super light.

Computer Science (23 courses)

CS50’s Introduction to Computer Science
Harvard University

Algorithms, Part I
Princeton University

Algorithms, Part II
Princeton University

Machine Learning for Data Science and Analytics
Columbia University

Bitcoin and Cryptocurrency Technologies
Princeton University

Artificial Intelligence (AI)
Columbia University

Reinforcement Learning
Brown University

Computer Architecture
Princeton University

Machine Learning
Georgia Institute of Technology

Enabling Technologies for Data Science and Analytics: The Internet of Things
Columbia University

Machine Learning
Columbia University

Analysis of Algorithms
Princeton University

Networks Illustrated: Principles without Calculus
Princeton University

Machine Learning: Unsupervised Learning
Brown University

CS50’s Computer Science for Business Professionals
Harvard University

CS50’s AP® Computer Science Principles
Harvard University

HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective)
Columbia University

Animation and CGI Motion
Columbia University

Networks: Friends, Money, and Bytes
Princeton University

CS50’s Understanding Technology
Harvard University

Data Structures and Software Design
University of Pennsylvania

Algorithm Design and Analysis
University of Pennsylvania

Computer Science: Algorithms, Theory, and Machines
Princeton University

Data Science (21 courses)

Statistical Thinking for Data Science and Analytics
Columbia University

Statistics and R
Harvard University

Introduction to Spreadsheets and Models
University of Pennsylvania

People Analytics
University of Pennsylvania

High-Dimensional Data Analysis
Harvard University

Introduction to Bioconductor: Annotation and Analysis of Genomes and Genomic Assays
Harvard University

Data Science: R Basics
Harvard University

Case Studies in Functional Genomics
Harvard University

Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Harvard University

Big Data and Education
Columbia University

Principles, Statistical and Computational Tools for Reproducible Science
Harvard University

Data Science: Inference and Modeling
Harvard University

Data Science: Visualization
Harvard University

High-performance Computing for Reproducible Genomics
Harvard University

Data Science: Linear Regression
Harvard University

Data Science: Capstone
Harvard University

Data Science: Wrangling
Harvard University

Data Science: Machine Learning
Harvard University

Data Science: Productivity Tools
Harvard University

Data Science: Probability
Harvard University

Data, Models and Decisions in Business Analytics
Columbia University

Shabbir Ahmed, starts 19 Aug 2018, edX, Deterministic Optimization, here.

Course Syllabus

Skip Syllabus DescriptionWeek 1

  • Module 1: Introduction
  • Module 2: Illustration of the Optimization Problems

Week 2

  • Module 3: Review of Mathematical Concepts
  • Module 4: Convexity

Week 3

  • Module 5: Outcomes of Optimization
  • Module 6: Optimality Certificates

Week 4

  • Module 7: Unconstrained Optimization: Derivate Based
  • Module 8: Unconstrained Optimization: Derivative Free

Week 5

  • Module 9: Linear Optimization Modeling – Network Flow Problems
  • Module 10: Linear Optimization Modeling – Electricity Markets

Week 6

  • Module 11: Linear Optimization Modeling – Decision-Making Under Uncertainty
  • Module 12: Linear Optimization Modeling – Handling Nonlinearity

Week 7

  • Module 13: Geometric Aspects of Linear Optimization
  • Module 14: Algebraic Aspect of Linear Optimization

Midterm

Week 8

  • Module 15: Simplex Method in a Nutshell
  • Module 16: Further Development of Simplex Method

Week 9

  • Module 17: Linear Programming Duality
  • Module 18: Robust Optimization

Week 10

  • Module 19: Nonlinear Optimization Modeling – Approximation and Fitting
  • Module 20: Nonlinear Optimization Modeling – Statistical Estimation

Week 11

  • Module 21: Convex Conic Programming – Introduction
  • Module 22: Second-Order Conic Programming – Examples

Week 12

  • Module 23: Second-Order Conic Programming – Advanced Modeling
  • Module 24: Semi-definite Programming – Advanced Modeling

Week 13

  • Module 25: Discrete Optimization: Introduction
  • Module 26: Discrete Optimization: Modeling with binary variables – 1

Week 14

  • Module 27: Discrete Optimization: Modeling with binary variables – 2
  • Module 28: Discrete Optimization: Modeling exercises

Week 15

  • Module 29: Discrete Optimization: Linear programming relaxation

  • Module 30: Discrete Optimization: Solution methods

Sidney R. Coleman, Harvard University Department of Physics,  Physics 253: Quantum Field Theory, here.

Professor Coleman’s wit and teaching style is legendary and, despite all that may have changed in the 35 years since these lectures were recorded, many students today are excited at the prospect of being able to view them and experience Sidney’s particular genius second-hand.

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