Internal training for Junior Software Engineers

Basic Math

Reference: Basic Math

• Linear Algebra
• Analytic Geometry
• Matrix Decompositions
• Vector Calculus
• Probability and Distribution
• Continuous Optimization

C++

Reference: cme212

• C++ keywords, identifiers and tokens
• Pointers, arrays, strings
• Memory management
• Functions
• Classes and namespaces
• Abstractions
• Templates and STL
  • Pair
  • vector
  • list, forward_list, deque, stack, queue, priority_queue
  • set, multiset, map, multimap, Unordered_set, unordered_map
• Inheritance and polymorphism
• Operator overloading
• Encapsulation
• Constructors and destructors
• Parallelism, multithreaded applications and concurrency
• Tools: UNIX, Git, Make, C++

Tools

• Introduction to GDB, valgrind, cachegrind (profiling optimization).

Algorithms

Reference: Course252

Textbook: Cormen, Leiserson, Rivest and Stein Introduction to Algorithms, 3rd edition.

• Algorithmic problems:
  • Sorting and searching
  • Graph algorithms:
    • Graph traversal (DFS, BFS) and applications
    • Connectivity, strong connectivity, bi-connectivity
    • Minimum spanning tree
    • Shortest path
    • Matchings
    • Network flow
  • Hard problems:
    • Traveling salesman problem
    • Longest path, Hamilton cycle
    • Boolean circuit satisfiability
    • Clique
    • Vertex cover
• Algorithm design:
  • Divide-and-conquer
  • Graph traversal
  • Greedy
  • Dynamic Programming
  • Reductions
  • Use of advanced data structures
• Algorithm correctness:
  • Proofs and proof techniques (assumptions, basic logic inference and induction)
  • Tree and graph properties that make graph algorithms work
  • When does the greedy algorithm work?
• Algorithm analysis:
  • Time and space complexity
  • Asymptotic analysis: Big Oh, Little oh, Theta
  • Worst case and average case analysis
  • Lower bounds
• Tractable and intractable problems:
  • Polynomial time algorithms
  • NP-algorithms
  • NP-hardness and NP-completeness
  • NP-Reductions

Python

Basic Robotics